Systems for and methods of a closed-loop drug delivery system

Abstract

Disclosed are systems and methods for adjusting an infusion rate of a drug being delivered to a patient. The method includes extracting a fluid sample from the patient, isolating a drug filtrate by processing the fluid sample within a first time period, and analyzing the drug filtrate for a drug concentration using chromatography system, a mass spectrometry system, or both within a second time period. The method further includes comparing, using a control process, the drug concentration with a target concentration range, and adjusting the infusion rate of the drug to ensure the drug concentration is within the target concentration range.

Description

SYSTEMS FOR AND METHODS OF A CLOSED-LOOP DRUG DELIVERY SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 527,168, filed on July 17, 2023, which is incorporated herein by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0002] N / ABACKGROUND

[0003] The present disclosure relates generally to systems and methods for drug delivery. More specifically, the present disclosure relates to systems and methods for providing closed-loop drug delivery and regulating drug infusion. This work was supported by the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana- Farber / Harvard Cancer Center.

[0004] Chemotherapeutic agents, like many drugs, are typically dosed based by body surface area (BSA) or weight. However, drug excretion and metabolism rates vary between patients; as a result, there is considerable variation in the metabolism of the active drug. For example, drug metabolism and excretion rates vary between two patients receiving the same dose of a drug, which can lead to the two patients having different blood concentration profiles of the drug resulting in different therapeutic outcomes. Moreover, within the same patient, the concentration of drug given by continuous infusion can drastically vary, in some patients with a 10-fold range, while BSA- based dosing assumes the concentration will be constant. The result of BSA-based dosing is that patients are commonly overdosed or underdosed, resulting in toxicity or lack of efficacy, respectively. Unfortunately, underdosing is often not recognized until follow-up testing weeks or months after initiating treatment, which can lead to tumor growth or metastasis.

[0005] However, there have been attempts to address issues caused by underdosing and overdosing. In particular, therapeutic drug monitoring (TDM) uses an assay to determine the blood concentration of chemotherapeutic agents and informs clinicians or technicians of how to adjust the dose for the next treatment cycle. One TDM study demonstrated that patients receiving TDMof 5-Fluorouracil (5-FU) had higher disease-free survival rates and fewer side effects compared to patients dosed via BSA. In addition, genotype guided dosing, where genes that are known to be associated with decreased metabolism of a chemotherapy are sequenced before starting therapy, and chronomodulation, where the infusion rate of a drug is changed as a function of time of day when the drug is most effective, have both been developed to improve the manner of dosing chemotherapies. However, despite improving clinical outcomes, these techniques are rarely implemented in clinical practice for chemotherapies, in large part due to the logistical issues inherently associated with such techniques. Therefore, there exists a need to develop interventions that allow for individualized drug dosing of chemotherapies, which can help maximize the number of patients that reach the most efficacious concentration level of drug in the blood while mitigating the risks of underdosing or overdosing.SUMMARY

[0006] The present disclosure provides systems and methods for adjusting an infusion rate of a drug being delivered to a patient to ensure a drug concentration is within a target concentration range. In accordance with one non-limiting example, a drug-delivery system may be used to extract a fluid sample and isolate a drug filtrate from the patient before analyzing the drug filtrate for a drug concentration using chromatography or mass spectrometry and adjusting the infusion rate based on the determined drug concentration. Accordingly, the infusion rate of a drug being delivered to a patient can be efficiently and accurately controlled, which in turn may decrease the likelihood that a patient will be underdosed or overdosed.

[0007] In accordance with one aspect of the disclosure, a method is provided for adjusting an infusion rate of a drug being delivered to a patient. The method includes extracting a fluid sample from the patient, isolating a drug filtrate by processing the fluid sample within a first time period, and analyzing the drug filtrate for a drug concentration using chromatography system, a mass spectrometry system, or both within a second time period. The method further includes comparing, using a control process, the drug concentration with a target concentration range, and adjusting the infusion rate of the drug to ensure the drug concentration is within the target concentration range.

[0008] In accordance with another aspect of the disclosure, a drug-delivery system for adjusting an infusion rate of a drug being delivered to a patient is provided including a fluid transport system having at least one inlet and at least one outlet, and a pump that is coupled to thefluid transport system, the pump being configured to draw a fluid sample from the patient through a first inlet of the fluid transport system. The drug-delivery system further includes a sample preparation system coupled to a first outlet of the fluid transport system, the sample preparation system being configured to receive the fluid sample and isolate a drug filtrate therefrom in a first time period. The drug-delivery system further includes an analyzer including a liquid chromatography column coupled to a mass spectrometer, the analyzer being configured to analyze the drug filtrate for a drug concentration in a second time period, and a controller coupled to the analyzer. The controller being configured to control a control process to compare the drug concentration with a target concentration range, and the controller is further configured to adjust the infusion rate of the drug through the fluid transportation system to ensure the drug concentration is within the target concentration range.

[0009] In accordance with yet another aspect of the disclosure, a drug-delivery method is provided to target a specific serum concentration range in a patient using a drug-delivery system. The method includes accessing a blood or serum from the patient, measuring an amount of drug in the blood or serum accessed from the patient, and controlling the drug-delivery system based on the amount of a drug in the blood or serum to target the specific serum concentration range in the patient.

[0010] The foregoing and other aspects and advantages of the present disclosure will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration one or more embodiment. These embodiments do not necessarily represent the full scope of the invention, however, and reference is therefore made to the claims and herein for interpreting the scope of the invention. Like reference numerals will be used to refer to like parts from Figure to Figure in the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.

[0012] FIG. 1 is a schematic illustration of the limitations of conventional BSA-based drugdelivery approaches, in accordance with aspects of the present disclosure.

[0013] FIG. 2 is a block diagram of a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0014] FIG. 3 is a flowchart of non-limiting example steps for a method of using a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0015] FIG. 4 is a flowchart of non-limiting example steps for a method of rapidly isolating a drug fdtrate from a blood sample obtained from a patient, in accordance with aspects of the present disclosure.

[0016] FIG. 5 is a schematic illustration of an example closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0017] FIG. 6 is a block diagram of an example closed-loop drug delivery system including a vacuum pump system and a syringe injector, in accordance with aspects of the present disclosure.

[0018] FIG. 7 is a cross-sectional view of an example mixer of a of a closed-loop drug delivery system implemented as millifluid mixer, in accordance with aspects of the present disclosure.

[0019] FIG. 8 is a top plan view of another example mixer of a closed-loop drug delivery system implemented as a T-shaped tubing adapter, in accordance with aspects of the present disclosure.

[0020] FIG. 9A is a calibration curve used to calibrate a closed-loop drug delivery system on day 0 of an example experiment, in accordance with aspects of the present disclosure.

[0021] FIG. 9B is a calibration curve used to calibrate a closed-loop drug delivery system on day 1 of the example experiment of FIG. 9A, in accordance with aspects of the present disclosure.

[0022] FIG. 9C is a calibration curve used to calibrate a closed-loop drug delivery system on day 2 of the example experiment of FIGS. 9A and 9B, in accordance with aspects of the present disclosure.

[0023] FIG. 9D is a calibration curve used to calibrate a closed-loop drug delivery system on day 3 of the example experiment of FIGS. 9A-9C, in accordance with aspects of the present disclosure.

[0024] FIG. 9E is a calibration curve used to calibrate a closed-loop drug delivery system on day 9 of the example experiment of FIGS. 9A-9D, in accordance with aspects of the present disclosure.

[0025] FIG. 10A is a schematic timeline of another example experiment used to guide a design of a controller for a closed-loop drug-delivery system, in accordance with aspects of the present disclosure.

[0026] FIG. 10B is a plot from a control experiment illustrating measured changes of a concentration of a chemotherapeutic agent over time in the experiment of FIG. 10A, in accordance with aspects of the present disclosure.

[0027] FIG. 10C is a plot of similar data to that of FIG. 10B but acquired from an experiment where CLAUDIA controlled the concentration of the drug, illustrating the measured changes of concentrations compared with setpoint concentration values, in accordance with aspects of the present disclosure.

[0028] FIG. 10D is another plot of similar data to that of FIG. 10B but acquired from an experiment where CLAUDIA controlled the concentration of the drug, illustrating the measured changes of concentrations, in accordance with aspects of the present disclosure.

[0029] FIGS. 11 A-l 1C are a series of plots illustrating measured changes of a concentration of a chemotherapeutic agent over time using continuous BSA-dosing via a constant-infusion system.

[0030] FIGS. 11D-11F are plots illustrating measured changes of concentrations of a chemotherapeutic agent over time using a step-function infusion profde, in accordance with aspects of the present disclosure.

[0031] FIGS. 11G-11I are plots illustrating differences in the measured concentrations of FIGS. 11 A-l IF using BSA-dosing and a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0032] FIGS. 11J-11L are plots illustrating measured changes of concentrations of a chemotherapeutic agent over time using a trapezoidal-like infusion profde, in accordance with aspects of the present disclosure.

[0033] FIGS. 11M-11O are plots illustrating measured changes of concentrations of a chemotherapeutic agent over time using a tent-like infusion profde, in accordance with aspects of the present disclosure.

[0034] FIGS. 12A-12C are plots illustrating measured changes of an infusion rate over time using the step-function profde of FIGS. 11D-11F, in accordance with aspects of the present disclosure.

[0035] FIGS. 12D-12F are plots illustrating measured changes of an infusion rate over time using the trapezoidal-like profile of FIGS. 11J-11L, in accordance with aspects of the present disclosure.

[0036] FIGS. 12G-12I are plots illustrating measured changes of an infusion rate over time using the tent-like profile of FIGS. 1 IM-110, in accordance with aspects of the present disclosure.

[0037] FIGS. 12J-12L are plots illustrating measured changes of an infusion rate over time in the presence of a disturbance, in accordance with aspects of the present disclosure.

[0038] FIG. 13 A is a schematic timeline of yet another example experiment used to guide a design of a closed-loop drug-delivery system, in accordance with aspects of the present disclosure.

[0039] FIGS. 13B-13D are plots illustrating measured changes of a concentration of a chemotherapeutic agent over time using BSA-based dosing in the example experiment of FIG. 13 A, in accordance with aspects of the present disclosure.

[0040] FIGS. 13E-13G are plots illustrating measured changes of a concentration of a chemotherapeutic agent over time using a closed-loop drug-delivery system in the example experiment of FIG. 13 A, in accordance with aspects of the present disclosure.

[0041] FIGS. 13H-13J are plots illustrating differences in the measured concentrations of FIGS. 13B-13G using BSA-dosing and a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0042] FIG. 14A is a calibration curve of irinotecan hydrochloride used to calibrate a closed- loop drug delivery system, in accordance with aspects of the present disclosure.

[0043] FIG. 14B is a calibration curve of doxorubicin hydrochloride used to calibrate a closed- loop drug delivery system, in accordance with aspects of the present disclosure.

[0044] FIG. 14C is a calibration curve of cyclophosphamide used to calibrate a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0045] FIG. 14D is a calibration curve of theophylline used to calibrate a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0046] FIG. 14E is a calibration curve of thiotepa used to calibrate a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0047] FIG. 15 is a plot illustrating a step response of concentration over time measured while identifying a linear model of a control algorithm, in accordance with aspects of the present disclosure.

[0048] FIG. 16 is a plot illustrating the magnitude and phase change of a frequency response based on the linear model of FIG. 15, in accordance with aspects of the present disclosure.

[0049] FIG. 17 is a pole-zero plot based on the linear model of FIG. 15, in accordance with aspects of the present disclosure.

[0050] FIGS. 18A-18C are plots illustrating measured changes of a concentration of a chemotherapeutic agent over time using body weight (BW)-based dosing, in accordance with aspects of the present disclosure.

[0051] FIGS. 18D-18F are plots illustrating measured changes of a concentration of a chemotherapeutic agent over time using a closed-loop drug-delivery system, in accordance with aspects of the present disclosure.

[0052] FIGS. 18G-18I are plots illustrating differences in the measured concentrations of FIGS. 18A-18F using BW-dosing and a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0053] FIGS. 18J-18L are plots illustrating infusion rates of closed-loop drug delivery system of FIGS. 18D-18F, in accordance with aspects of the present disclosure.

[0054] FIG. 19 is a plot illustrated percent change in flow rate over time for three mixers, in accordance with aspects of the present disclosure.

[0055] FIG. 20A is a plot illustrating variability of 5-FU clearance among patents with identical BSA, in accordance with aspects of the present disclosure.

[0056] FIG. 20B is a plot illustrating circadian rhythm changes of 5-FU concentration during continuous infusion, in accordance with aspects of the present disclosure.

[0057] FIG. 21 is a schematic illustration of an experimental setup for testing a closed-loop drug delivery system, in accordance with aspects of the present disclosure.

[0058] FIG. 22 is a plot illustrating changes in percent of original flow rate over time using coated and non-coated tubing during blood sampling, in accordance with aspects of the present disclosure.

[0059] FIG. 23 is a schematic illustration of an automated closed-loop drug delivery system, in accordance with aspects of the present disclosure.DESCRIPTION

[0060] Before any aspects of the disclosure are explained in detail, it is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The present disclosure is readily extended to other aspects and implementations and may be practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. Unless specified or limited otherwise, the terms “mounted,” “connected,” “supported,” and “coupled” and variations thereof are used broadly and encompass both direct and indirect mountings, connections, supports, and couplings. Further, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings.

[0061] As used herein in the context of computer implementation, unless otherwise specified or limited, the terms “component,” “system,” “module,” “controller,” “framework,” and the like are intended to encompass part or all of computer-related systems that include hardware, software, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a processor device, a process being executed (or executable) by a processor device, an object, an executable, a thread of execution, a computer program, or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components (or system, module, and so on) may reside within a process or thread of execution, may be localized on one computer, may be distributed between two or more computers or other processor devices, or may be included within another component (or system, module, and so on).

[0062] In the methods described herein, the steps can be carried out in any order without departing from the principles of the disclosure, except when a temporal or operational sequence is explicitly recited. Recitation in a claim to the effect that first a step is performed, and then several other steps are subsequently performed, shall be taken to mean that the first step is performed before any of the other steps, but the other steps can be performed in any suitable sequence, unless a sequence is further recited within the other steps. For example, claim elements that recite “Step A, Step B, Step C, Step D, and Step E” shall be construed to mean step A is carried out first, step E is carried out last, and steps B, C, and D can be carried out in any sequence between steps A andE, and that the sequence still falls within the literal scope of the claimed process. A given step or sub-set of steps can also be repeated.

[0063] Furthermore, specified steps can be carried out concurrently unless explicit claim language recites that they be carried out separately. For example, a claimed step of doing X and a claimed step of doing Y can be conducted simultaneously within a single operation, and the resulting process will fall within the literal scope of the claimed process.

[0064] The term “substantially” as used herein refers to a majority of, or mostly, as in at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99.5%, at least about 99.9%, at least about 99.99%, or at least about 99.999% or more.

[0065] The following discussion is presented to enable a person skilled in the art to make and use aspects of the disclosure. Various modifications to the illustrated configurations or processes will be readily apparent to those skilled in the art, and the generic principles herein can be applied to other aspects and applications within the scope of the present disclosure and the understanding of one of skill based thereon. Thus, the present disclosure is not intended to be limited to particular embodiments or aspects shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. The following detailed description is to be read with reference to the figures, in which like components or elements in different figures have like reference numerals. The figures, which are not necessarily to scale, depict selected aspects and configurations or processes and are not intended to limit the scope of the disclosure. Skilled artisans will recognize the examples provided herein have many useful alternatives and fall within the scope of the disclosure.

[0066] In accordance with aspects of the present disclosure, mechanisms (which can, for example, include systems, methods, and media) for adjusting an infusion rate of a drug being delivered to a patient based on a current drug concentration in the patient’s blood are provided.

[0067] Generally, the present disclosure provides systems, methods, and media for using a closed-loop drug delivery device that can advantageously extract and isolate a drug filtrate for a blood sample of a patient, analyze the drug filtrate for a drug concentration, and adjust the infusion rate of the drug being delivered to the patient based on the determined drug concentration. In particular, aspects of the present disclosure provide systems, methods and media for adjusting an infusion rate of a drug to ensure that the drug concentration is with a target concentration range.

[0068] In some non-limiting examples, a drug-delivery system may be configured as a closed- loop drug-delivery device that includes a sample preparation system and an analyzer. The sample preparation system may be capable of rapidly isolating a drug filtrate from a blood sample within a first time period, and the blood sample can be extracted from a patient, e.g., using a pump in a fluid transportation system. In this way, a drug filtrate can be quickly isolated and subsequently transferred to the analyzer system. In some aspects, the analyzer is capable of rapidly determining a drug concentration within a second time period, . In one non-limiting example the first time period may be less than about three minutes. In another non-limiting example, the second time period may be less than about four minutes. Irrespective of a particular amount of time for the first or second time period, it may be desirable to keep the time under about 3 to about 8 minutes. However, this or any other amount of time may be flexible to include longer times, for example, including up to or exceeding 20-30 minutes. Accordingly, the time needed to isolate and drug filtrate and determine a concentration of a drug being infused in a patient can be greatly reduced, which in turn can provide more timely data when determining how to adjust the infusion rate of the drug. In this way, more accurate and effective drug infusion may be permitted, as having more timepoints can enable better performance of the controller, thus leading to a reduction in chronic underdosing and / or overdosing. This in turn can increase overall patient safety and satisfaction.

[0069] To that end, a drug-delivery system may further include a controller that is configured to control an infusion rate of the drug. In some aspects, the controller can be configured to control a control process to compare the measured drug concentration with a target concentration range. Accordingly, informing the controller and control process with timely and relevant data on the current concentration of the drug in a patient’s bloodstream may further enhance the efficacy of the drug-delivery system and allow the system to quickly adjust an infusion profile to stay within optimal parameters. In addition, the drug delivery system can comprise commercially available components which may ease availability of the system and decrease the manufacturing costs thereof. Therefore, the use of a closed-loop drug-delivery system in accordance with aspects of the present disclosure can be rapidly clinically translatable and increase dosing efficacy while enhancing patient safety and satisfaction.

[0070] FIG. 1 illustrates the limitations associated with conventional BSA-based dosing or drug-delivery methods. As discussed above, BSA-based drug-delivery fails to account for interindividual variabilities in pharmacokinetic (PK) profiles in patients with the same BSA. Putanother way, two patients with the same ideal-BSA may receive the same dose of chemotherapy, even if several other physiological differences exist between the two patients. For example, a first patient 104 may be shorter and weigh more than a second patient 108, but each patient may have the same calculated BSA, leading to the same dose being recommended for each patient. Thus, the dose given to the patients may have entirely different results for the first patient 104 than the second patient 108 as a result of the PK variabilities between one another. This is particularly the case when delivering chemotherapeutic agents or drugs to patients, such as 5 -Fluorouracil (5-FU). PK variability may result from a variety of different sources, including but not limited to circadian rhythm 112, genetic polymorphism 116, and sex differences 120. Furthermore, concentrations of a chemotherapeutic drug can vary in a 10-fold range under the assumptions of BSA-based drugdelivery, which can result in harmful and even potentially lethal side effects for some patients.

[0071] Referring now to the non-limiting example illustrated in FIG. 2, a closed-loop drugdelivery system (e.g., the drug delivery system 200) can provide frequent feedback on a current concentration of a drug in a patient which can be used to adjust an infusion rate of the drug and account for PK variability between different patients. In some aspects, a drug-delivery system can be configured to deliver a drug to a patient, extract blood from a patient to determine a current drug concentration of the patient, compare the drug concentration to an optimal concentration range, and update the delivery rate of the drug to ensure the drug concentration stays within the optimal concentration range. In some aspects, the drug-delivery system can include a fluid transport system that fluidly connects the patient to the drug-delivery system, meaning that a blood sample from the patient can be drawn through the fluid transport system. It is contemplated that the fluid transport system can have any number of suitable inlets or outlets to transport fluid (e.g., blood, a drug in fluid form, or another fluid), including an outlet or inlet connected to an external reservoir, such as a reservoir containing a drug solution. The drug-delivery system can further include a sample preparation system which is configured to isolate a drug filtrate from the blood sample, and an analyzer that is configured to determine a drug concentration based on the drug filtrate. The determined drug concentration data can be input to a controller included in the drugdelivery system, and the controller can be configured to compare the infusion rate of the drug with a target drug concentration range, and alter the infusion rate to ensure that the drug concentration stays or is brought within the target drug concentration range.

[0072] In some aspects, a target drug concentration range can be a range of drug concentrations around a recommended drug concentration (e.g., a recommended dose) that are acceptable or optimal for a patient to receive a correct dose of the drug. In this way, keeping a concentration of a drug being delivered to a patient decreases the likelihood of the patient experiencing the negative side effects associated with chronic overdosing or underdosing (e.g., administering a drug concentration that is outside the target drug concentration range). For example, a target drug concentration range can be a range between about 75% and about 125%, between about 90% and about 110%, between about 95% and about 105%, between about 97% and about 103%, between about 98% and about 102%, between about 99% and about 101%, or between about 99.5% and about 100.5% of a recommended drug concentration.

[0073] In some aspects, a closed-loop drug-delivery system 200 can include a fluid transport system 204, a sample preparation system 208, an analyzer 212, and a controller 216. The fluid transport system 204 can have an external inlet 220 and an external outlet 224 which are fluidly connected to a patient 228 (e.g., an arm of a patient). The fluid transport system 204 can draw a blood sample of the patient 228 into the drug-delivery system 200 and, more specifically, provide the blood sample to the sample preparation system 208. As will be discussed below in greater detail, the sample preparation system 208 may rapidly isolate a drug filtrate from the blood sample using one or more filtering and / or mixing techniques, and the resultant drug filtrate can be provided directly to the analyzer 212 or indirectly through the fluid transport system 204 to the analyzer 212. Put another way, the sample preparation system 208 may be coupled directly to the analyzer 212 such that the sample preparation system 208 and the analyzer 212 are in direct fluid communication with one another. In some examples, the drug-delivery system 200 can include a liquid handler to automatically transfer the drug filtrate to the analyzer 212. Alternatively, it is contemplated that the drug filtrate may be manually transferred from the sample preparation system 208 to the analyzer 212.

[0074] Still referring to FIG. 2, the analyzer 212 can be configured to analyze the drug filtrate and determine a drug concentration therefrom as will be discussed below in greater detail. Further, the analyzer 212 can provide the drug concentration to the controller 216, and the controller 216 can be coupled to the fluid transport system 204, the analyzer 212, and, optionally, the sample preparation system 208. In some aspects, the controller 216 executes or controls a control process which evaluates if the drug concentration determined by the analyzer 212 is within a predeterminedoptimal concentration range, and the controller 216 can regulate delivery or infusion rate of the drug through the fluid transport system 204 to ensure that the drug concentration stays within the bounds of the optimal concentration range. In this way, the drug delivery system 200 can provide automatic control of drug delivery based on the patient’s 228 reception of the drug (e.g., the patient’s 228 tolerance or uptake of the drug) to increase the efficacy of the drug delivery procedure.

[0075] In some aspects, a fluid transport system can be a system of tubes, catheters, and the like, which are capable of transporting fluid. In particular, the fluid transport system 204 can include the external inlet 220 through which the blood sample is drawn from the patient 228 into the drug-delivery system 200, and an external outlet 224 through which a fluid can be delivered to the patient 228. In some aspects, the fluid that can be delivered to the patient 228 can include a drug. In particular, the fluid can include one or more chemotherapeutic drugs, such as alkylating agents, nitrosoureas, antimetabolites, topoisomerase inhibitors, mitotic inhibitors, antitumor antibiotics, or another type of chemotherapeutic drug. For example, the fluid can include 5- fluorouracil, 6-mercaptopurine, atezolizumab, nivolumab, pembrolizumab, rucaparib, niraparib, Olaparib, ixazomib, or any combination thereof. Other drugs may also be used. That is, the systems and methods described herein are not limited to a particular drug or clinical application, but can be applied to additional drug classes, including any drugs given intravenously, including antibiotics.

[0076] In addition, the fluid transport system 204 can include one or more internal inlets 232 and one or more internal outlets 236 which may facilitate fluid communication to, from, and / or between the other components in the drug-delivery system 200. In some aspects, the fluid transport system includes a pump 240 housed therein or fluidly connected thereto. The pump 240 can be configured to draw the blood sample through the external inlet 220 and simultaneously provide the fluid containing a chemotherapeutic drug to the patient 228 through the external outlet 224. However, it is contemplated that more than one pump may be used in the fluid transport system 204, such as a first pump to draw the blood sample and a second pump to provide the fluid. In some aspects, the pump 240 is a peristaltic pump, an electronic pump, an isocratic pump, a piston pump, a programmable syringe pump, and / or a continuous pump. Accordingly, the pump 240 may include multiple pumps that can be used to control deliveries of multiple drugs. As will bediscussed below in greater detail, the pump 240 can be controlled according to a variety of different infusion profiles of the drug(s) e.g., delivery profiles of the drug(s) being delivered).

[0077] Still referring to FIG. 2, the sample preparation system 208 can receive the blood sample from the patient 228 via the fluid transport system 204. A sample preparation system can include one or more components housed therein and connected to one another, and the one or more components may be configured to isolate a drug filtrate from a blood sample obtained from a patient. For example, the sample preparation system 208 can include a centrifuge 244, a vortex mixer 248, an optional microfluidic mixer 252 (which may be omitted), and a syringe filter 256. In some aspects, the centrifuge 244 can be a mini centrifuge. As will be discussed below in greater detail, the sample preparation system 208 and the components included therein can isolate a drug filtrate from the blood sample obtained from the patient 228 and provide the drug filtrate to the analyzer 212.

[0078] Correspondingly, an analyzer can comprise one or more sensor and / or filtering components connected to one another that may be configured to analyze a drug filtrate to determine a drug concentration in a patient. For example, the analyzer 212 can include a chromatographysensor system. The chromatography-sensor system may include, for example, a high-performance liquid chromatography (HPLC) system coupled with an ultraviolet-visible (UV-Vis) range, an inductively coupled plasma mass spectrometry( ICP-MS) system, or another type of chromatography system. In the illustrated, non-limiting example, the analyzer 212 can include a liquid chromatography column 260 that is directly coupled to a mass spectrometer 264, or the liquid chromatography column 260 can be indirectly coupled to (e.g. , in fluid communication with) the mass spectrometer 264 via the fluid transport system 204. The drug filtrate can be received by the liquid chromatography column 260, eluted through the liquid chromatography column 260, and subsequently received and analyzed by the mass spectrometer 264. In particular, the drug filtrate can be passed through the liquid chromatography column 260 to perform an isocratic elution of the drug filtrate. In some aspects, the liquid chromatography column can be an HPLC and / or a Cl 8 hydrophilic interaction liquid chromatography (HILIC) column, and the mass spectrometer 264 can be a nebulizer electrospray ionization (ESI) accurate mass time-of-flight (TOF) mass spectrometer. However, it is contemplated that a variety of mass spectrometer techniques can be used to accurately determine the drug concentration of the drug filtrate, including triple quadrupole mass spectrometry (3Q), quadrupole-TOF (Q-TOF) spectrometry,and / or orbitrap spectrometry. Further, a mass spectrometer may have a resolution of at least 0.01 m / z.

[0079] Relatedly, a controller can receive a drug concentration provided as data from a mass spectrometer, and the controller can be configured to execute a control process to ensure that the drug concentration stays within or is altered to be within a target drug concentration range. For example, the controller 216 can receive the electronic data containing the drug concentration from the analyzer 212 and execute a control process to use the drug concentration data to regulate the infusion rate of the drug to the patient. In particular, the controller 216 can be configured to compare the drug concentration with the target drug concentration range and compute an error therefrom. Further, the controller 216 can be configured to alter the infusion delivery or infusion rate of the drug to minimize the computed error, thus maximizing the efficiency of drug-delivery and preventing a patient from being underdosed or overdosed.

[0080] It is contemplated that the controller 216 may be a proportional (P) controller, an integral (I) controller, a derivative (D) controller, a model predictive controller, or any combination thereof. For example, the controller 216 can be a PID controller with a derivative filter governed by the equation below, or another form of the PID control algorithm with a derivative filter:In addition, the controller 216 can have a plurality of controller gains which determine the controller’s 216 control of the infusion rate of the drug in response to receiving a drug concentration that is not within the target concentration range. For example, the controller 216 can include a first gain KP, a second gain Ki, and a third gain Ka. As will be discussed below in greater detail, the controller gains KP, Ki, Ka of the controller 216 can be implemented using an adaptive tuning approach or process, meaning that the gains KP, Ki, Ka may be reduced by a gain factor if the drug concentration exceeds a predetermined upper threshold and / or increased by a gain factor if the drug concentration falls below a predetermined lower threshold.

[0081] As discussed above, the controller 216 can execute a control process to determine if an infusion rate of the drug being provided to the patient should be altered. The infusion rate may be controlled by the fluid transport system 204 and the pump 240, and the controller 216 can be configured to control the fluid transport system 204 and change the infusion rate, if necessary.Further, it is contemplated that the infusion rate of the drug can have one or more different concentration-time flow profiles, such as a continuous rate infusion profile, step-function infusion profile, chronomodulated infusion profile, trapezoid-like (z.c., trapezoidal) infusion profile, tentlike (i.e., triangular) infusion profile, peristaltic infusion profile, and / or another type of infusion profile. Accordingly, the controller 216 can be configured to control the fluid transport system 204 to modify any infusion profile or switch between different infusion profiles to ensure that the drug concentration is within the target drug concentration. As discussed above, it is contemplated that the drug-delivery system 200 can be used to deliver one or more subsequent drugs to a patient. As such, the controller 216 can be configured to control the fluid transport system 204 to modify one or more subsequent infusion rates for one or more subsequent drugs in addition to the infusion rate for the drug, meaning that the controller 216 can control the infusion of multiple drugs to a patient simultaneously.

[0082] FIG. 3 illustrates a method of determining a drug concentration in a patient via a blood sample obtained from the patient and updating the infusion rate of the drug based on the determined drug concentration, according to some aspects of the present disclosure. As discussed above, the infusion rate of a drug can be updated periodically based on a most recent drug concentration in a patient’s bloodstream to ensure that the drug concentration is within a target drug concentration range. In some aspects, the drug concentration can be determined by analyzing a drug filtrate, which may be isolated from a blood sample obtained from the patient. For example, a process 300 of updating an infusion rate based on a determined concentration of a patient’s blood sample can include first extracting a blood sample from the patient using a fluid transport network included in a drug-delivery system at step 304. As discussed above, extracting the blood sample can include drawing blood through an external inlet (e.g., a catheter) of the fluid transport network and providing the blood sample to a sample preparation system. In some examples, step 304 can include automatically extracting a blood sample from the patent at predetermined time intervals. In particular, a controller can actuate a pump (e.g., a peristaltic pump) to draw a blood sample from the patient every few minutes (e.g., between every three minutes and between every five minutes) and backflush the fluid transportation network after each time interval. That is, a controller can actuate the pump on a predetermined sampling interval schedule to automatically draw blood from a patient.

[0083] At step 308, the process 300 can include isolating a drug filtrate from the blood sample using the sample preparation system. In some aspects, isolating the drug filtrate includes a variety of sub-steps to remove cells proteins, lipids, and / or additional confounding substances present in the blood sample, as will be described in greater detail below. In some aspects, the sample preparation system can automatically isolate a drug filtrate from the blood sample using a liquid handler or an internal liquid transportation system, which can decrease filtration time and lead to faster drug sampling and concentration adjustment.

[0084] Still referring to FIG. 3, the process 300 can further include analyzing the drug filtrate to determine the drug concentration using an HPLC-MS analyzer at step 312. As discussed above, a drug-delivery system can include an HPLC or HILIC column and a mass spectrometer which may collectively define an analyzer of the system. In some aspects, step 312 includes further isolating the drug filtrate by performing an isocratic elution of the drug filtrate through a HILIC column before using the mass spectrometer to determine the drug concentration. It is contemplated that multiple types of mass spectrometry can be used to determine the drug concentration by analyzing the drug filtrate, including 3Q mass spectrometry, TOF mass spectrometry, Q-TOF mass spectrometry, and / or orbitrap mass spectrometry. In some examples, the analyzing the drug filtrate is performed automatically after the drug filtrate has been created at step 308. In particular, a sample preparation system can be in communication with a controller that includes one or more control algorithms (e. ., monitoring software) to provide automatic, real-time drug concentration determination.

[0085] At step 316, the process 300 further includes comparing the drug concentration with a target concentration range using a control process. As discussed above, a controller connected to the drug-delivery system can be used to automatically execute or run the control process. In some aspects, the control process compares the calculated drug concentration e.g., the current drug concentration) with a target drug concentration range and computers the error therebetween. Further, controller can execute the control process at step 320, to automatically adjust the infusion rate of the drug, thereby ensuring that the drug concentration stays or is brought within the target drug concentration range. For example, the control process can instruct the fluid transport system to increase the infusion rate (e.g., a flow rate) of the drug to the patient if the drug concentration is determined to be below a lower threshold of the target drug concentration range, or the control process can instruct the fluid transport system to decrease the infusion rate of the drug to the patientif the drug concentration is determined to be below a lower threshold of the target drug concentration range. In addition, step 320 can optionally include adjusting one or more subsequent infusion rates for one or more subsequent drugs being delivered to a patient as discussed above.

[0086] In some examples, the control process(es) can include different mathematical models and prediction techniques to accurately evaluate the effect of infusion rate on drug concentration within a patient. The control process may include modeling a patient’s PK profile as a first order system and determining parameters of the first order system (e.g., time constant and gain) using a least squares estimator based on drug concentration and time data obtained in step 316. These newly determined parameters can be continuously updated during sampling and drug infusion after a predetermined time period (e.g., after 15 minutes, or after 30 minutes, or after 32 minutes, or after 35 minutes) to allow the system to build an accurate model of a patient’s PK profile.

[0087] In some aspects, step 308 e.g., isolating a drug filtrate from a blood sample) can be performed within a first time period, and step 312 (e.g., analyzing the drug filtrate for a drug concentration) can be performed within a second time period. For example, the first time period may be less than about 1200, less than about 600 seconds, less than about 300 seconds, less than about 240 seconds, less than about 180 seconds, less than about 140 seconds, or about 130 seconds, and the second time period may be less than about 1200 seconds, 600 seconds, less than about 300 seconds, less than about 270 seconds, less than about 240 seconds, or about 240 seconds. Accordingly, the process of isolating a drug filtrate from a blood sample and analyzing the drug filtrate to determine a drug concentration may be completed between about every 180 seconds and about every 480 seconds (e.g., between about every 3 minutes and about every 8 minutes). In this way, the drug-delivery system disclosed herein can provide accurate estimates of a drug concentration in a patient’s bloodstream about every 8 minutes, thereby allowing the controller and control system to adjust the infusion rate of the drug more frequently than conventional sample preparation techniques. Moreover, it is contemplated that any combination of the steps of the process 300 can be integrated in a fully automated system to streamline drug concentration estimation and infusion rate adjustment (i.e., various components of a drug delivery system can be in communication with a controller that can automate one or more components of the system). For example, a controller may automate at least one of a pump to draw a fluid sample from a patient, a sample preparation system to isolate a drug filtrate from a fluid sample, and / or an analyzer to analyze a drug filtrate for a drug concentration. In this way, the drug concentration in a patientduring drug infusion can be more efficiently and accurately controlled, which in turn may decrease the likelihood that a patient will be underdosed or overdosed.

[0088] FIG. 4 is a non-limiting example of processes of isolating a drug filtrate from a blood sample, according to some aspects of the present disclosure. A first process 400 can first include obtaining a blood sample at step 404. As discussed above, the blood sample can be drawn from a patient using a fluid transport system in a drug-delivery system. At step 408, the first process 400 can include centrifuging the blood sample or, as just one non-limiting example, about 30 seconds (or greater or lesser) to isolate blood plasma from the blood using a centrifuge. At step 412, the first process 400 can include adding the resultant supernatant (e.g., the blood plasma) to an extraction solvent and mixing to extract a first supernatant using a microfluidic mixer, vortexing the mixture for about five seconds to isolate a second supernatant using a vortex mixer, and centrifuging the mixture for an additional 30 seconds to isolate a third supernatant. At step 416, the first process 400 can include filtering the supernatant to isolate the drug filtrate. For example, the third supernatant can be filtered through a syringe filter to isolate the drug filtrate. In some aspects, the first process 400 may be completed in less than about 180 seconds, or in about 130 seconds.

[0089] Alternatively, a second process 420 can first include obtaining a blood sample at step 424 similar to step 404 of the first process 400 as discussed above. At step 428, the second process 420 can instead include adding an extraction solvent directly to the blood sample to isolate a first supernatant and vortexing the first supernatant for about five seconds to isolate a second supernatant at step 428. At step 432, the second process 420 can include centrifuging the second supernatant for about 30 seconds to isolate a third supernatant. At step 436, the second process 420 can include filtering the supernatant to isolate the drug filtrate. For example, the third supernatant can be filtered through a syringe filter to isolate the drug filtrate, as discussed above. In some aspects, the second process 420 may be completed in less than about 180 seconds, or in less than about 130 seconds.

[0090] It is contemplated that the extraction solvent can be any suitable solvent for removing lipids, cells, proteins, and other interfering substances from the blood sample. For example the extraction solvent can be a 25 / 75 methanol / acetonitrile (v / v) mixture, and the extraction solvent can be mixed in a 6: 1 ratio to the blood sample or blood plasma. Further, it is contemplated, that the syringe filter can be a polytetrafluoroethylene (PTFE) filter, hydrophilic filter, hydrophobicfilter, polypropylene filter, a polyethersulfone (PES) filter, mixed cellulose ester (MCE) filter, or another type of filter.

[0091] FIG. 5 is a schematic illustration of an example closed-loop drug-delivery system according to aspects of the present disclosure. In some aspects, a drug-delivery system 500 can include a patient 504, a sample preparation system 508, an HPLC-MS sensor 512, a control process 516, and an adaptive tuning PID controller 520. A blood sample can be drawn from the patient 504 and provided to the sample preparation system 508, which can prepare a clean drug filtrate sample. The drug filtrate sample can then be provided to the HPLC-MS sensor 512, which can further isolate the drug filtrate of interest before determining or measuring a drug concentration via mass spectrometry. The measured drug concentration can then be provided as data to the control process 516, and the control process 516 can compare the measured drug concentration with a desired drug concentration (e.g., a target drug concentration range as discussed above). When comparing the measured and desired drug concentrations, the control process 516 can compute an error between the concentrations, and the computed error can be provided to the PID controller 520. The PID controller 520 can further execute the control process 516 to determine an updated infusion rate of the patient 504 based on the computed error, and the updated infusion can be implemented to minimize the computed error (e.g., to bring the measured drug concentration within the target drug concentration range). In this way, the drug concentration and infusion rate of a drug can be regulated by the drug-delivery system 500. In some aspects, the drug-delivery system 500 is capable of completing a full loop (e.g., extract the blood sample, process and measure the blood sample for a drug concentration, execute the control process, and update the infusion rate) within a time period that is less than 8 minutes, or that is about 8 minutes. Accordingly, the drug-delivery system 500 is capable of updating the infusion rate periodically at 8 minute intervals, thus increasing drug-delivery accuracy and subsequently decreasing the likelihood that the patient will experience underdosing and / or overdosing.

[0092] FIG. 6 is a block diagram of another example closed-loop drug-delivery system that can automatically draw a blood sample from a patient, according to aspects of the present disclosure. In some aspects, the drug-delivery system 600 can be similar to the drug-delivery system 500 illustrated in FIG. 5. In particular, the drug-delivery system 600 illustrated in FIG. 6 can include a patient 604, a sample preparation system 608, and / or a sensor 612 (e.g. , an HPLC / MS sensor and / or a gas chromatograph (GC) GC / MS sensor). It is contemplated that the drug-deliverysystem 600 can also include a control process and / or a controller (e.g., an adaptive tuning controller, a PI controller, a PID controller, etc.). Further, a blood sample can be drawn from the patient 604 and provided to the sample preparation system 608, which can prepare a clean drug fdtrate sample. The drug fdtrate sample can then be provided to the sensor 612, which can further isolate the drug filtrate of interest before determining or measuring a drug concentration via mass spectrometry.

[0093] In some examples, a sample preparation system can include components to automatically draw a blood sample from a patient and prepare the blood sample for drug filtrate isolation. In particular, a sample preparation system may include pumping and / or mixing systems to prevent blood from coagulating and creating blockages within a drug-delivery system. As illustrated in the non-limiting example of FIG. 6, the sample preparation system 608 can include a degassing system 616, a syringe system 618, and a mixer 620 to help prevent blood from coagulating within the drug-delivery system 600 during and after sampling. Further, the sample preparation system 608 can include a first pump 624 (e.g., a peristaltic pump) to draw a blood sample from the subject 604. In some examples, the first pump 624 can draw blood from the patient 604 according to a pulsatile profile, although it is contemplated that the first pump 624 can also be controlled according to another sampling profile (e.g., a continuous sampling profile).

[0094] Further, the first pump 624 can pump the blood sample into the sample preparation system 608 via a fluid transport system 628 (e.g., a plurality of inlets, outlets, tubes, capillaries, etc.). In particular, the fluid transport system 628 can provide the blood sample to the degassing system 616, which can include a valve 632 and / or a second pump 636. In some aspects, the valve 632 can be a bleed valve that exhausts or vents gas that is trapped in the blood sample. For example, the valve 632 can be implemented as a bleed valve with a gas permeable, but liquid impermeable membrane that allows gas to be exhausted while the blood sample is retained within the sample preparation system 608. In some examples, the second pump 636 can be a vacuum pump that can apply a pressure (e.g., negative pressure and / or a pressure gradient) to the valve 632 to further encourage air to be exhausted from the blood sample. Thus, the valve 632 and the second pump 636 of the degassing system 616 can cooperate with one another to remove air from the blood sample, which in turn can decrease the risk of embolism during sampling and lead to greater patient safety and satisfaction.

[0095] Once the blood sample has been degassed (e.g, via the degassing system 616), the first pump 624 can provide the blood sample to the mixer 620, where the blood sample can be combined with additional fluids to prevent coagulation. Specifically, the syringe system 618 can be configured to provide an anticoagulant 640 to the mixer 620. In some examples, the syringe system 618 includes a third pump 644 (e.g., a syringe pump, a DC brushless motor, a stepper motor, a linear stepper motor, etc.) that can pump the anticoagulant 640 into the mixer 620. That is, the third pump 644 can be used to selectively insert and / or retract a plunger of a syringe (e.g. , a syringe containing the anticoagulant 640) to drive the anticoagulant 640 into the mixer 620. In some examples, the third pump 644 pumps the anticoagulant 640 according to a pulsatile profile, similar to that of the blood sampling provided by the first pump 624. Accordingly, the anticoagulant 640 can be pumped into the mixer 620 at discrete time intervals. It is contemplated that the anticoagulant 640 can include any suitable compound, such as Warfarin, Heparin, Ethylenediaminetetraacetic (EDTA), Dabigatran, Rivaroxaban, Apixaban, Edoxaban, Enoxaparin, Dalteparin, Fondaparinux, etc.

[0096] Relatedly, the syringe system 618 can further include one or more syringes containing saline 648, and the third pump 644 can selectively pump saline 648 into the mixer 620 to flush the sample preparation system 608. Flushing the sample preparation system 608 with saline 648 can help prevent debris buildup therein, further reducing the risk of embolism. Further, saline flushing can increase pulsatile sampling efficiency by keeping tubing (e.g., tubing of the fluid transportation system 628) clear in between sampling intervals. Accordingly, the third pump 644 may be configured to pump the anticoagulant 640 and saline 648 through the sample preparation system 608 in an alternating cycle, although it is contemplated that saline 648 can also be pumped through the sample preparation system 608 before and after sampling.

[0097] With continued reference to FIG. 6, the mixer 620 can combine or mix the blood sample with the anticoagulant 640 before the blood sample exits the sample preparation system 608. In some examples, the mixer 620 is provided as a microfluidic mixer, a millifluidic mixer, a vortex mixer, a centrifuge, a tubing adapter, and / or any combination thereof, as will be discussed below in greater detail. Mixing the blood sample with the anticoagulant 640 can help prevent the blood sample from coagulating, which can improve the efficiency and longevity of the drug-delivery system 600 by reducing blockages and / or debris within the tubing of the drug-delivery system 600. The resulting blood sample can then be provided to the sensor 612 to determine a drugconcentration therein (z.e., a concentration of a drug within the patient 604). In some examples, the blood sample is transferred to the sensor 612 via a liquid handler, or the blood sample is pumped through a capillary tube 652 that couples the sensor 612 to the mixer 620. It will be understood that the blood sample can also be subject to one or more filtering and / or extraction steps before being provided to the sensor 612, such as the drug fdtration steps discussed above.

[0098] In this way, a drug-delivery system can prevent coagulation and streamline mixing to allow blood samples to be rapidly prepared for downstream analysis. As discussed above, a sample preparation system can include a mixer that combines a blood sample with a drug (e.g., an anticoagulant). It is contemplated that a mixer can be an active mixer (e.g., incorporating a pump) or a passive mixer that uses, for example, capillary action to combine a blood sample and a drug. Referring now to the non-limiting example illustrated in FIG. 7, an example mixer 700 is illustrated that can be provided as a microfluidic or millifluidic mixer. The mixer 700 can include at least one inlet 704 to receive a blood sample and / or a compound (e.g., an anticoagulant and / or saline) and at least one outlet 708 to direct the resulting mixture downstream along a sample preparation system. In particular, the mixer 700 can include a first inlet 704A to receive a blood sample (e.g., a blood sample taken from a patient via a catheter) and a second inlet 704B to receive an anticoagulant and / or saline. Each inlet 704 can define a corresponding fluid channel 712 through the mixer 700, the first inlet 704A defining a first channel 712A and the second inlet 704B defining a second channel 712B. In some aspects, the channels 712 can converge at a joint 716 to define a mixing channel 720 that extends between the joint 716 and the outlet 708.

[0099] It is contemplated that the mixing channel 720 can define any suitable profile to promote mixing, such as linear profiles, arcuate profiles, or both. Specifically, the mixing channel 720 can define a tortuous path that can include a winding profile defined by a plurality of turns to promote mixing. In the illustrated non-limiting example, the mixing channel 720 can define a substantially serpentine profile including a plurality of rounded turns. Thus, during operation of a sample preparation system, a blood sample can be provided to the first inlet 704A and a fluid containing an anticoagulant can be provided to the second inlet 704B. The blood sample can flow along the first channel 712A and the anticoagulant can flow along the second channel 712B before converging at the joint 716. The blood sample and the anticoagulant can then flow along the shared mixing channel 720, and the serpentine profile defined by the mixing channel 720 can help to mixthe blood sample with the anticoagulant before the resulting mixture exits the mixer 700 at the outlet 708.

[0100] After a blood sample exits the mixer 700, saline can be introduced at the second inlet 704B and used to flush the channels 712, 720 to prevent debris from building up and inhibiting flow through the mixer 700. In this way, the mixer 700 can provide a passive way of mixing a blood sample with one or more compounds to prepare the blood sample for downstream processing and analysis. It is contemplated that the mixer 700 can comprise any suitable and / or biocompatible material, such as, for example, aluminum, silicone, stainless steel, a polymer, resin, etc. In some examples, the mixer 700 includes a protective coating to prevent interactions between a blood sample and the channels defined by the mixer. Correspondingly, the mixer 700 may be manufactured via 3D printing, injection molding, stamping, pressing, casting, machining, etc.

[0101] As discussed above, a mixer can also be implemented as a tubing adapter Referring now to FIG. 8, another example mixer 800 is illustrated which can be a tubing adapter (e.g., a T- shaped tubing adapter). For example, the mixer 800 can include at least one inlet 804 (e.g., a first inlet 804A and a second inlet 804B) and at least one outlet 808. In some examples, the inlets 804 and the outlet 808 can be defined by a body 812 such that the inlets 804 can be in direct fluid communication with the outlet 808. Further, the mixer 800 can include at least one fastening member 816 that can be inserted into the inlets 804 and / or the outlet 808. In some examples, the at least one fastening member 816 can serve as a fitting that provides an airtight and / or watertight seal between the mixer 800 and other components of a drug-delivery system (e.g., a catheter, a fluid transportation system, a capillary tube, a syringe, a pump, etc.)

[0102] In the illustrated non-limiting example, the mixer 800 can include a first fastening member 816A coupled to a first inlet 804 A, a second fastening member 816B coupled to a second inlet 804B, and a third fastening member 816C coupled to the outlet 808. Further, the first inlet 804A can receive a fluid sample (e.g, blood drawn from a catheter and / or provided along a fluid transportation system coupled to the first fastening member 816A) from a subject, and the second inlet 804B can receive a fluid containing a compound (e.g. , an anticoagulant and / or saline provided by a syringe coupled to the second fastening member 816B). During sampling, the blood sample and the fluid containing anticoagulant can be pumped through the inlets 804 and mixed within a passageway defined by the body 812 before the resulting mixture exits the mixer 800 at the outlet 808. In this way, the mixer 800 can provide a passive way of mixing a blood sample with one ormore compounds to prepare the blood sample for downstream processing and analysis. It is contemplated that the mixer 800 can comprise any suitable and / or biocompatible material, such as, for example, aluminum, silicone, stainless steel, a polymer, resin, etc. In some examples, the mixer 800 includes a protective coating to prevent interactions between a blood sample and the channels defined by the mixer. Correspondingly, the mixer 700 may be manufactured via 3D printing, injection molding, stamping, pressing, casting, machining, milling, etc.EXAMPLES

[0103] To experimentally demonstrate the feasibility and advantages of the systems and methods provided herein, an experimental drug-delivery system was created that included the architecture described above with respect to FIG. 2.

[0104] Overview

[0105] A closed-loop drug delivery system was developed to allow physicians to maintain the concentration of drugs in their patient’s blood within their therapeutic window without burdening health care workers. While multiple closed-loop systems for chemotherapy have been reported in the literature to successfully control the concentration of drug in vivo, no system has successfully reached the clinic, in part due to technical challenges with the microfluidic and nucleic-acid based sensors used in these systems. While these systems were able to maintain drug levels within the target range in vivo, they have several shortcomings that could make clinical translation difficult. First, aptamer-based sensors foul within a short time period when exposed to whole blood. Indeed, anti-coagulants were needed to be administered to the rabbits to enable the blood to flow for the duration of their experiment, as will be discussed in greater detail below. Additionally, a unique aptamer needs to be designed, validated, and manufactured for each new target drug. Third, the channels in the microfluidic systems for these sensors themselves can clog after prolonged use, resutling in the sensors not being robust.

[0106] To that end, a system that overcame the challenges discussed above was developed that could also be rapidly translated to the clinic. CLAUDIA (Closed-Loop Automated Drug Infusion regulAtor) takes blood from the patient, rapidly performs sample preparation of blood to extract the drug for analysis on HPLC-MS, and inputs the concentration of the drug into a control algorithm, which compares the current concentration with the target concentration (e.g., computes the error), and then adjusts the infusion rate of the drug to keep its concentration within thetherapeutic window, as illustrated in FIG. 5. To address the shortcomings of previously published closed-loop systems, high performance liquid chromatography-mass spectrometry (HPLC-MS) was used as the detection system in the closed-loop system. Multiple types of MS would be appropriate for this application, including triple quadrupole MS (3Q), time of flight (TOF) MS, quadrupole-TOF (Q-TOF) MS, and orbitrap MS as they are highly specific and reliable methods of detecting drugs and other molecules extracted from blood.

[0107] In particular, CLAUDIA was constructed using an Agilent 1290 UHPLC system, the system including a binary pump, autosampler, and temperature-controlled column compartment, coupled to a dual-nebulizer ESI ‘accurate mass’ TOF mass spectrometer. Additionally, a SeQuant ZIC-HILIC column (50 * 4.6 mm, 3.5 pm, 200 A) was used as the HPLC column. A controller was developed to run a control process capable of adjusting the infusion rate of CLAUDIA in response to the determination of a drug concentration using HPLC-MS, as discussed below in greater detail.

[0108] In order to distinguish drugs of interest from background signal in the serum, it is important to have a specificity and sensitivity sufficient to detect the drug of interest over background. Based upon the work discussed herein, it has been found that a resolution of at least 0.01 m / z may be needed for some drugs using a HPLC-TOF, such as 5-fluorouracil. HPLC-MS has been trusted by the FDA to determine the profile of various drugs during clinical trials. An additional benefit of using HPLC-MS is that MS is capable of simultaneously measuring the concentration of multiple drugs, which would enable the present approach to be slightly tuned to enable the system to control the concentration of another drug or simultaneously control the concentration of multiple drugs in combination drug regimens.

[0109] One critical component of the present system was a method to rapidly extract drug from the blood and analyze it on HPLC-MS. After the blood is removed from the patient, the drug must be extracted from the blood before the concentration of drug in the blood can be determined via HPLC-MS. The sample preparation process removes the cells, proteins, lipids, and additional substances present in the blood that would damage the HPLC-MS and reduce the signal from the drug.

[0110] Traditional sample preparation processes take between 30-60 minutes; for example, commonly the blood is first centrifuged to remove the cells and then liquid-liquid extraction, protein precipitation, solid phase extraction, or an orthogonal technique is performed on theresulting plasma to remove the proteins, lipids, and other substances present. The 30-60-minute timeframe is too long to be used in the present closed-loop system, since the longer the time between sampling blood and changing the infusion rate if the concentration is too high or low, the worse the closed-loop system is able to control the levels of drug in the patient. Moreover, for drugs with a short half-life, this delay in obtaining the concentration could result in large inaccuracies in predicting the concentration of drug at the present time.

[0111] Methods

[0112] A rapid sample preparation process was developed that takes 2 minutes and 10 ± 3 seconds (N=24) to perform, as illustrated in FIG. 4. In this process, blood is centrifuged for 30 seconds to separate plasma from the blood cells. Then the plasma is added to a 25 / 75 methanol / acetonitrile (v / v) mixture with 5-bromouracil (an internal standard chosen for its structural similarity to 5-FU) in a 1 to 6 ratio (v / v), vortexed for 5 seconds, centrifuged for 30 seconds, and the resulting supernatant is filtered through a 0.45 pm PTFE filter before being placed on the HPLC-MS. The resulting clear filtrate was then run immediately on the HPLC-MS. An even more rapid method was also developed where whole blood is mixed with 25 / 75 methanol / acetonitrile (v / v) mixture with 5-bromouracil, vortexed, centrifuged for 30 seconds, filtered, and then run on the HPLC-MS.

[0113] In addition to needing to develop a rapid sample preparation method to extract the drug from blood, it was also necessary to develop a rapid HPLC-MS method to separate 5-FU from other compounds that were extracted from the blood during the sample preparation steps. In that regard, a 4-minute method was developed using a HILIC column and isocratic method. Thus, it became possible to measure the concentration of drug every 4-minutes with an overall delay of 8- minutes in the present system.

[0114] After developing both the sample preparation and HPLC-MS methods, the accuracy and amount of noise (e.g, precision) expected within the sensing component of CLAUDIA was determined, as illustrated in FIGS. 9A-9E and Tables 1-4 below. Notably, though FIGS. 9A-9E illustrate calibration data, quality control (QC) samples were also analyzed, which further support that the CLAUDIA system is capable of accurately determining the concentration of the drug. The QC samples are identical to the calibration control samples shown and described with respect to FIGS. 9A-9E, except that they were created independently to demonstrate the ability of CLAUDIA to accurately determine the concentration of a given sample.

[0115] FIGS. 9A-9E are plots of calibration curves from five days (day 0, day 1, day 2, day 3, and day 6, respectively) in which the above methods were validated.Table 1Table 2Table 3

[0116] The method validation results demonstrated that a linear calibration curve was obtained from 0.5-40 mg / L, which covers the clinically relevant concentration range, including the target range of 2.5-3 mg / L. Accordingly, it is possible to reliably determine the concentration of 5- fluorouracil (5-FU, a commonly used chemotherapy) over this range, as shown in Table 2.

[0117] Additionally, a preliminary disturbance rejection closed-loop experiment was performed with CLAUDIA using the control algorithm that was used for the setpoint experiments,and it was found that the impact of the DPD inhibitor on the PK of 5-FU resulted in a sufficiently increased gain of the system to create an unstable system, as illustrated in FIG. 10. In particular, FIG. 10A illustrates a schematic of the experiment, FIG. 10B illustrates a plot of continuous rate infusion determined by BSA following the scheme in FIG. 10A (N=l). The pump turned off around T50, which resulted in no 5-FU being infused during this interval. Thus, the datapoints from times -45-75 minutes were artificially low because the pump malfunctioned during that time and did not infuse the drug. A preliminary (N=l) CLAUDIA disturbance experiment that informed the design of the controller is illustrated in FIGS. 10C and 10D.

[0118] Since patients with this range of PK variability will receive 5-FU, a method was developed to account for these PK differences in real time in order to allow for the system to work for a range of patients. Specifically, a PID controller was implemented by using the backward Euler method. To tune the controller, the system was modeled as a first-order system, using a continuous rate infusion experiment to determine the gain and time constant of the system, as will be discussed below in greater detail. SIMULINK (version R2020b, 10.2) was used to simulate the system and tune the controller. Both the 8-minute time delay present in the current system and the 4-minute sampling rate were simulated. In addition, noise was added to the measurements to mimic the noise seen in concentration measurements with HPLC-MS. The controller was tuned by first finding the values of Kp and Ki that would result in instability of the system in a proportional-only or integral-only system, respectively. The values of both these controller gains were reduced by at least half, and then tuned KD and N were tuned empirically via simulations. The controller gains were altered until an acceptable rise time (e.g., under 1 hour) was achieved that resulted in a stable system under a range of system conditions (e.g, system gains and time constants) to mimic the widespread PK variability observed clinically.

[0119] To prevent issues that may arise due to limitations associated with the infusion system (e.g, due to the pump leaking), as a safety measure a rule-based component of the controller was implemented. The rule was triggered if the concentration decreased by 50% or more, while the infusion rate increased for each of the past two timepoints. If this condition were to be met, then the infusion rate is kept constant, as the positive feedback loop could result in a runaway infusion rate. Clinically, when this rule is triggered, the system could also trigger an alarm to alert the patient’s caregivers that there is a problem with the infusion system. The following gains were chosen: Kp: 0.2, Ki: 0.0003, KD: 15, andN (derivative filter): 0.009. These controller gains allowedfor sufficient control of 5-FU concentrations under a range of 5-FU PK models in the rabbits according to the simulations.

[0120] Thus, an adaptive PID controller approach was implemented, where the controller gains (kP, ki, kd) were reduced by a gain-factor, depending on if the concentration of 5-FU exceeded a predetermined threshold for two different timepoints during the treatment regimen. These gainfactors were determined by understanding that the steady-state concentration is directly proportional to the system’s gain. Originally, a proportional gain-factor was applied to counteract these increases in controller-gain (c. ., if the concentration exceeds the target concentration twofold, the gain-factor would be V2). After performing simulations for these situations, it was decided to make the gain-factors slightly stronger, as shown below in Table 5 (e.g, the gain-factor of U was applied to concentrations over 5 mg / L instead of 5.5 mg / L).Table 5

[0121] Example 1

[0122] Once CLAUDIA was developed, its ability to bring the concentration of 5-FU to the clinically determined optimal concentration of 5-FU for humans (2.5-3.0 mg / L) was compared to BSA-based dosing in rabbits, as illustrated in FIGS. 11 A-l IF. In the BSA-based dosing group, the concentration at steady-state ranged from 2.5-6 mg / L, as illustrated in FIGS. 11A-11C, while all CLAUDIA groups reached the target of 2.5-3 mg / L at steady-state, as illustrated in FIGS. 11D- 1 IF. Qualitatively, it was clear that the CLAUDIA groups were capable of bringing the concentration of 5-FU nearer to the target concentration compared to the BSA-based group, as illustrated in FIGS. 11 A-l IF. Moreover, the average deviation from the target range was 0.25 ± 0.03 mg / L (mean ± standard deviation) for the CLAUDIA group versus 0.98 ± 0.93 mg / L in the BSA-based group, as illustrated in FIG. 11H, and the percent of time within the target window was on average 45 ± 3% for the CLAUDIA group versus 13 ± 18% for the BSA-based dosing group. CLAUDIA resulted in an AUC within the target range for all three experiments, whileBS A-based dosing resulted in no experiments being in the target range, with one group being under the target and two being above the target range, as illustrated in FIG. 111.

[0123] After evaluating CLAUDIA’s ability to bring the concentration of 5-FU to the target concentration in a step function concentration-time profile, its ability to change the concentration of 5-FU was also evaluated according to additional concentration-time profiles, as illustrated in FIGS. 11J-11O. 5-FU, like many other chemotherapies, has been shown to have differences in effectiveness in killing cancer cells as a function of the time of day the chemotherapy is given to the patient Thus, CLAUDIA’s ability to control the concentration of 5-FU was investigated in two different concentration-time profiles, which could be used clinically to give 5-FU via a chronomodulated profile, and demonstrated that CLAUDIA could control the concentration of 5- FU according to both a trapezoid-like and tent-like shaped concentration-time profile.

[0124] These results demonstrate that CLAUDIA is capable of both increasing the amount of time that 5-FU is within the target concentration, and in decreasing the extent that the concentration varies from the target concentration when it is out of range. Moreover, the concentration in the CLAUDIA groups both overshot and undershot the target concentration near steady-state due to the action of CLAUDIA, which resulted in the AUC being within the target range, while all the BSA-groups were almost exclusively over or under the target range, which resulted in AUC values above or below the target, respectively. It is important to note that one group in the BSA group illustrated in FIG. 11 A performed as well as the CLAUDIA groups in terms of average deviation as illustrated in FIG. 11G, and nearly as well in terms of percent of time in range as illustrated in FIG. 11H, however, it underperformed in terms of area under the curve (AUC) compared to CLAUDIA as illustrated in FIG. 111.

[0125] This underscores the fact that if a clinician guesses the dose of chemotherapy correctly, the concentration-time profde for that patient can be within the target range according to some metrics. However, if the patient does not guess correctly when dosing 5-FU on a BSA-basis as illustrated in FIGS. 11B and 11C, the deviation and time in range can be significantly worse. In contrast, CLAUDIA consistently gave approximately the same average deviation from target and percent of time in range among all three groups as illustrated in FIGS. 1 ID and 1 IF, demonstrating its ability to keep the concentration of drug within the target window regardless of the exact PK differences. Indeed, the infusion rate to the rabbits in the CLAUDIA groups all differed from what would have been given if they were treated on a BSA-basis. If they were dosed on a BSA-basis(their masses were 3.95, 4.10, 4.08 kg), they would have received an average dose of 1.78, 1.82 and 1.81 pl / second, but they actually received average doses of 1.27 ± 0.27, 1.31 ± 0.34, and 1.25 ± 0.36 pl / second, respectively (mean ± standard deviation), from times 32-188 minutes (e.g., when the system was at steady state) as illustrated in FIGS. 12A-12I.

[0126] Further, FIG. 12 illustrates infusion rates for the CLAUDIA experiments discussed above. In particular, FIGS. 12A-12I illustrate infusion rates for the step experiment corresponding to FIGS. 11 A-l 1C, FIGS. 12D-12F illustrate infusion rates for the tent experiment corresponding to FIGS. 11J-11L, and FIGS. 12G-12I illustrate infusion rates for the step experiment corresponding to FIGS. 1 IM-110.

[0127] Example 2

[0128] After demonstrating the ability of CLAUDIA to perform well in setpoint tracking experiments, it was next necessary to investigate if the system could still control the levels of 5- FU when the PK of the rabbits was altered during the experiment. In order to achieve this goal, (E)-5-(2-bromovinyl)uracil — an inhibitor to dihydropyrimidine dehydrogenase (DPD), the enzyme that metabolizes 5-FU — was administered to alter the PK of 5-FU, as illustrated in FIG. 13A. The control was then performed, being BSA-based dosing of 5-FU by continuous rate infusion (CRI) in rabbits administered DPD inhibitor, as illustrated in FIGS. 10B-10D. In these experiments, it was observed that the concentration of 5-FU was >18 mg / L in all experimental groups and reached a maximum concentration of 22 mg / L in two groups, which is >15 mg / L or over 7-fold above the target concentration range of 2.5-3 mg / L, which importantly was reached via BSA-based dosing without the DPD inhibitor, as illustrated in FIG. 12A.

[0129] Significantly, the variation in 5-FU PK with and without the DPD inhibitor replicates the >8-fold variability of 5-FU observed clinically. While the entire BSA-based dosing group reached concentrations of>18 mg / L when the DPD inhibitor was added (target: 2.5-3 mg / L), when CLAUDIA was allowed to control the concentration of 5-FU, the concentration of 5-FU only exceeded 7 mg / L at one timepoint and it did not exceed 5 mg / L at any other time, as illustrated in FIGS. 10E-10G. Importantly, CLAUDIA decreased the flow rate of 5-FU as a function of time to keep the concentration of 5-FU within the target range, demonstrating that the ability to stay within the target range was due to the action of the controller, as illustrated in FIGS. 12L12L. As shown in FIGS. 10E-10G, the CLAUDIA group deviated much less from the target range than the BSA- based group; the average deviation was 0.35 ± 0.22 and 7.87 ± 1.92 mg / L, respectively, asillustrated in FIG. 13H. As shown in FIG. 131, CLAUDIA increased the time in range from 2.50% ± 2.89% in the BSA-based group to 32.43% ± 8.58% of the time.

[0130] Lastly, the area under the curve was decreased from 1632.9 ± 348.5 mg*min / L in the BSA-based group to 544.6 ± 28.2 mg*min / L in the CLAUDIA group (target: 440-528 mg*min / L), as illustrated in FIG. 13J. Importantly, the adaptive tuning method prevented the third disturbance CLAUDIA experiment from becoming unstable, as illustrated in FIG. 13G. Indeed, the system was beginning to experience unstable oscillations, but after the adaptive control algorithm went into effect, the system stabilized and the concentration monotonically returned to the target range.

[0131] Example 3

[0132] One of the key benefits to using HPLC-MS as the sensor is that it can be directly applied to multiple other chemotherapies, which could enable CLAUDIA to control the concentration of other drugs independently as well as multiple drugs simultaneously. To demonstrate the ability of CLAUDIA to detect the concentration of multiple drugs simultaneously, theophylline, thiotepa, cyclophosphamide, irinotecan and doxorubicin were spiked together into blood, and the same sample preparation technique that was optimized for 5-FU was performed. Next, the concentration of the drugs was analyzed with a 3.5-minute HPLC-MS method that was developed for detecting these multiple chemotherapies simultaneously. The results demonstrate that it is possible to measure the concentrations of multiple chemotherapies simultaneously using the rapid sample preparation and HPLC-MS method, as shown in Table 6 below and in the calibration curves corresponding to different chemotherapy drugs illustrated in FIG. 14.To determine this, blood was spiked with multiple chemotherapies mixed together and the multiple drugs individually to investigate if mixing all the drugs together altered the ability to measure theconcentration of the drug accurately, and it was found that spiking multiple drugs together did not decrease the accuracy of the sensor.

[0133] In that regard, chemotherapeutic drugs were separated and quantified on an Agilent 1290 liquid chromatography system coupled to a 6224 A time-of-flight mass spectrometer (TOF MS, Agilent Technologies, Santa Clara, CA, USA). Separations were performed on an Agilent Poroshell 120 SB-Aq column (2.1 * 50 mm, 1.9 pm dp), held at 25 °C, by injecting 5 pL of sample and eluting with a mobile phase of 0.1% formic acid in water (v / v, A) and acetonitrile (B) at a flow rate of 1 mL / min. The gradient program used was 0 min, 2% B; 0.5 min, 2% B; 3.5 min, 100% B. The dual electrospray ionization source used nitrogen gas at 350 °C, 11 L / min, at a pressure of 40 psig. The capillary voltage was set to 3500 V and the fragmentor voltage to 150 V. Mass spectra were acquired from 100 to 3000 m / z at a rate of 2 Hz. TOF acquisition was made in the positive mode. A continuous flow of a reference mixture through the second source nebulizer was used to calibrate the mass axis at 121.0509 m / z and 922.0098 m / z. Data processing of the output signal was performed using MassHunter Quantitative Analysis software (Version 10.1). The parameters listed below in Table 7 were used in the analysis:Table 7A 100-ppm m / z tolerance was used for all compounds. A 9-point calibration curve with 1 / x- weighted linear regression was used to generate calibration curves by quantifying extracts of spiked blood at known concentration levels from 0.5 mg / L to 60 mg / L.

[0134] The method validation was performed as was the case for 5-FU, except for these samples, three independent stock solutions of the multiple different chemotherapies were mixed together to create three independent stock solutions. The calibration curves were created with thesestock solutions, and the QCs that were produced using all the chemotherapies mixed together were made using a fourth independent stock solution made by mixing all the chemotherapies together before spiking blood. For the single chemotherapy QCs, blood was spiked with a single chemotherapy to identify if mixing the chemotherapies together impacted the results. 8- chlorotheophylline at 5 ppm in the extraction solvent served as the internal standard for all samples.

[0135] Example 4

[0136] Controller Experimentation

[0137] Advantageously, CLAUDIA may be able to function for all patients despite large degrees of variability in the dynamics of drug absorption between patients. Specifically, previous work has found that the time constant and gain associated with absorption to vary amongst different individuals. In order to account for this variability, the control algorithm(s) disclosed herein can constantly update an estimate of the patient’s absorption dynamics based on the history of drug infusion and the measured concentration throughout the treatment. These measurements can be used to fit a model using linear regression.

[0138] From the CRI experiments that were performed, the following linear model was identified as a simple model which was able to capture the observed dynamics:

[0139] In the above equation, A and B are parameters which account for the gain and time constant of the plant, respectively. The discrete domain was chosen due to the impact of sampling time and processing delays. For the experiments discussed below, the sampling time was 5 minutes and there was a 10 minute delay after a sample is taken before the data is available to the algorithm due to the time it takes to process the blood. FIGS. 15-17 illustrate an example step response, bode plot, and pole-zero plot generated from this model form, respectively. In these examples, the coefficients of A and B were 0.02 and 0.589, respectively. The sampling delays discussed above led to having extra poles at the origin. In order to fit a model of this form to the measured data, a linear regression was performed to determine coefficients which minimize the least squares error between model and measurement. This is done by shifting vector of measurement data to account for the sample delay and then creating linear vectors based on the infusion rate, which map to coefficients A and B.

[0140] To control the system response, a PI controller of the following form was used:

[0141] The controller was directly designed in the discrete domain and implemented via difference equations. Given the estimated plant parameters, the frequency at which the plant has (JI 10 degrees of phase was solved first. While the discrete time delays did not impact the gain of the plant, they did cause the plant phase to reach (-)540 degrees at fl = n. Therefore, the phase of the plant was determined to be (-) 110 degrees at a frequency despite the existence of only one non- origin pole. Further, the following equation was solved numerically to find the desired crossover frequency:

[0142] Once the crossover frequency was solved, the value of Ki was solved, in which the phase contribution from the PI controller was (-)10 degrees at the crossover frequency. The phase for the controller was modeled as:

[0143] The last step was to solve for Kp such that the return ratio would pass through unity gain at the specified crossover frequency, as illustrated in the equations below:

[0144] The controller was implemented via the difference equations and a new infusion rate was calculated following each measurement. The integrator was reset when the measured concentration exceeded the desired concentration to limit the amount of overshoot above the target concentration. Another aspect of this controls problem was the desire to stay below the target concentration and inability to apply negative control effort. For these reasons, several modifications were made to the classical PI controller in order to reduce the probability of overshoot. These include:1. A calibration period at the beginning of the experiment in which short-duration boluses were administered open-loop and the response was recorded. Given the variability in gain of the plant, the open-loop input was chosen such that the target concentration would not be exceeded. After the calibration period is complete, the PI controller was turned on with controller gains determined based on the recorded measurements from the calibration period.2. The integrator in the PI controller was reset when the target concentration was reached to limit overshoot.3. To minimize overshoot, the rate of increase of the infusion rate was also limited based on the estimated model parameters. To do this, a maximum desirable control effort was calculated with the following equation:Where e[fc] was the error between reference point and measured concentration, Glf= 4(1 — B), and Ghf = AB.The infusion rate administered was the minimum of this infusion rate and the infusion rate calculated from the PI controller.

[0145] Moreover, it was demonstrated that CLAUDIA can successfully control the concentration of irinotecan in vivo in 3 separate experiments, as illustrated in FIG. 18. Three control experiments were performed where irinotecan was given by a constant rate infusion (CRI) (see FIGS. 18A-18C). Irinotecan’s concentration-time profiles varied between different animals, although they were given the same dose on a bodyweight (BW) basis. It was found that CLAUDIA could bring the concentration of irinotecan to the target range within 90-minutes for all rabbits using the updated version of the controller, as illustrated in FIGS. 18D-18F. CLAUDIA reduced the root mean squared error from the target concentration from ~12 mg / L to ~2 mg / L (see FIG. 18G), increased the amount of time spent in the target range (see FIG. 18H), and decreased the area under the curve (AUC) from greatly overdosing all three rabbits in the CRI group, to achieving an AUC near the target AUC in all three rabbits dosed via CLAUDIA (see FIG. 181).

[0146] The infusion pump nearly turned off after 90-minutes as the drug had reached the target concentration, and only gave small periods of drug infusion over the next 2.5 hours when theconcentration began to fall excessively (see FIGS. 18J-18L). However, irinotecan is often given over a 90-minute infusion clinically, and thus CLAUDIA enabled the concentration of irinotecan to be brought to the target concentration range within this time frame. Thus, the control algorithm was shown to be effective for irinotecan dosing in the 90-minute infusion setting. However, it is contemplated that the control algorithm could be further developed to reduce overshooting of the target concentration, with the tradeoff that it will take longer to reach the target concentration. This more conservative controller may be useful if it is deemed unacceptable for a patient to have a concentration of drug above a given target concentration at any time during the infusion, and that it is possible for them to remain in the infusion center for up to 4 hours.

[0147] Mixing Experimentation

[0148] The system described herein included a method to mitigate blood coagulation during normal operation. In one example, Ethylenediaminetetraacetic acid (EDTA) was mixed with blood drawn from a catheter from a test animal. Initially, EDTA was chosen to minimize the potential for blood clotting in swine tests. Other potential anticoagulant candidates include heparin. In both cases, the device was designed to minimize the likelihood of backflow into humans. The system also had the capability of being flushed with a saline source to enable pulsatile sampling and to reduce the presence of blood in the tubing. A degassing strategy was defined in this system as well to minimize the likelihood of embolism.

[0149] To minimize dead space, the system included a series of low dead-space volume (LDV) fittings to ensure minimal fluid retention and minimal blood removal. The mixing system had minimal stagnation points and was engineered to reduce the likelihood of stagnation at the junctions. To promote consistent laminar flow for the blood-EDTA mixture and to minimize turbulence, the mixer was manufactured with the smallest diameter channels possible. The final mixer design (see FIG. 7) had a volume of 85 pl, although the mixer volume could be less than 1 pl, limited by manufacturing technology. Testing demonstrated that mixer materials could impact the rate of coagulation, particularly metals. In one example, the mixer materials were chosen as resins found in 3D printed materials. In another example, mixer materials were selected for their simultaneous compatibility with blood and ease of machinability, such as Polyether ether ketone (PEEK).

[0150] In yet another example, the mixer was simplified to a standard T-adapter fitting, as illustrated in FIG. 8. This application included flangeless fittings with a 14-28 screw thread, and aflat-bottom. These were used in conjunction with 1 / 16” outer diameter (OD) tubing. The tubing chosen for the system had similar requirements; in one example, tubing with an inner diameter (ID) of 0.19 mm was selected, although the tubing ID may be smaller depending on manufacturing capabilities. Materials that have been demonstrated to be successful for this system included Pharmed BPT polymer, which can reduce the risk of blood coagulation and increases the longevity of use in a peristaltic pump. Other examples may use materials like stainless steel tubing or mixers, with the addition of an inhibitory coating that prevents interactions between the blood and the mixer or tubing walls.

[0151] To reduce the risk of embolism, a gas bleed valve was included in the system as close to the sampled entity (i.e., the subject) as possible. In one example, the gas bleed valve was as simple as a T-fitting with a Gore-Tex membrane to vent trapped air, or it was more complex with the addition of a different pressure source across the membrane to encourage the evacuation of trapped air. The data illustrated in FIG. 19 was collected by using the simple T-adaptor described above. Blood was drawn via a peristaltic pump from a vein in a live pig for 30 seconds that input the blood into the T-adaptor, then the peristaltic pump was stopped, the tubes were flushed with saline at 2 ml / min for 1.5 minutes (3 ml total normal saline) by flushing with a syringe pump connected to another inlet to the 3-way T-adaptor, and then this process repeated. The data illustrated in FIG. 19 demonstrates the repeatability of these experiments.

[0152] Example 5

[0153] Chemotherapeutic agents are typically dosed based on body surface area (BSA), which is often estimated from the patient’s height and weight; however, this does not take into account interindividual variabilities in pharmacokinetics. As illustrated in FIG. 20A, drug metabolism and excretion rates vary between individuals: two patients receiving the same dose of a drug can have different blood concentration profiles of the drug which can lead to different therapeutic outcomes. Moreover, within the same patient, the concentration of drug given by continuous infusion can drastically vary (see FIG. 20B) in some patients (e.g. a 10-fold range), while BSA-based dosing assumes the concentration will be constant. The result of BSA-based dosing is that patients are commonly overdosed or underdosed, resulting in toxicity or lack of efficacy, respectively. Unfortunately, underdosing is often not recognized until follow-up testing weeks or months after initiating treatment, and can lead to tumor growth or metastasis. This highlights the need to develop a device that maintains the plasma concentration of drugs within their therapeutic window.

[0154] A closed-loop system, whereby the concentration of drug is continuously measured and the infusion rate of a drug is adjusted, can keep the concentration of the chemotherapeutic agent within a specific therapeutic window. In a closed-loop drug delivery system, a sensor can measure the concentration of the drug, a controller can calculate the rate of drug administration, an actuator can be a drug infusion pump that delivers the drug to an animal or human (specifically, the animal or the human’s circulatory system, see FIG. 21). Closed-loop drug delivery devices have already reached the clinic: the artificial pancreas system for type II diabetes, is the best-known example. To enable such a closed-loop system (e.g., CLAUDIA) to be developed, different components were developed to perform the required functions, which can include a pump to draw blood form the patient, a rapid blood prepressing system, the HPLC-MS system, and a control algorithm. First, a system was developed to draw blood from the patient in an automated fashion. In the simplest form of such a system, blood was drawn continuously from the patient. Importantly, blood was drawn at a low enough flow rate so that the cumulative amount of blood removed from the patient did not result in adverse effects for the patient. 20-30 ml of total blood drawn is reasonable for this patient demographic. To accomplish this, a peristaltic pump was used to draw blood from a jugular catheter in a swine and passed through a 0.89 mm inner diameter silicone tube. Even after coating the tube with polyethylene glycol (PEG), the flow through the tube stopped after approximately 45 minutes, as illustrated in FIG. 22. After the drug was drawn from the body, it was extracted from the blood before the concentration of drug was determined via HPLC-MS.

[0155] The performance of the controller was improved to make it robust and reliable to a broader number of situations that may be experienced clinically. This was performed, in part, due to the wide range of PK variability experienced clinically. An adaptive tuning algorithm was used to decrease the gains for the controller elements (i.e., the proportional, integral, and derivative gains) if the concentration of 5-FU exceeded a pre-determined threshold in a series of proof-of- concept experiments. To further improve the dosing methods discussed herein, an advanced control algorithm was developed that enabled the PK of the patient to be estimated during approximately the first 30-minutes of the drug infusion. The parameters in the controller were then adjusted to enable the controller to function well for that patient. Given the extent of PK variability observed clinically, the ability to tune the controller to a given patient’s PK is advantageous to improve dosing efficacy.

[0156] Moreover, a blood drawing module was developed that drew blood from the patient every four minutes and backflushed the line to prevent clotting after each blood draw. Thus, CLAUDIA can utilize an automated blood sampling process. The system was designed to draw blood from a patient’s catheter through a 0.89 mm inner diameter, PEG-coated, silicone tube for 30 seconds, collect the blood in an EDTA collecting vial, and then backflush the tube with saline until the next timepoint. A T-junction was used at the distal end of the tubing, with one outlet going to the collecting tube and the other outlet going to the saline flush. When the system was in a blood collection mode, the saline was not administered, resulting in the blood going to the collecting tube. During the backflushing step, saline was flushed through the tube into the patient, driven by a syringe pump. In some cases, a T-junction with actuatable valves can be used. Further, a minimal dead volume size was used to prevent stagnation points, which could result in blood clot formation that would both stop flow through the tubing and be dangerous to the patient during the backflush step. To draw the blood from the patient, a peristaltic pump was used.

[0157] Once this system was developed, its ability to draw blood over extended periods of time was evaluated in a swine model. Pig blood is known to clot quicker than human blood on average and thus swine blood serves as a good model for testing this blood drawing module. In these experiments, a Yorkshire swine was sedated with Telazol (tiletamine plus zolazepam 5 mg kg), xylazine (2 mg kg), and atropine (0.04 mg kg, and maintained with isoflurane. A jugular catheter was positioned to mimic a central port that chemotherapy patients receive, and then the pumping system was connected to this catheter. The volumetric / mass flow rate was measured for the duration of the experiment. A consistent flow rate over the duration of the experiment suggested that the system would be capable of consistently drawing blood without issues of clotting. In addition, a chemotherapy drug was administered to the swine to determine if the blood pump altered the concentration measurements by comparing the concentration of the drug in the blood drawn from the blood collection pump to the concentration of the drug in blood drawn manually from a femoral intravenous line in the swine at the same timepoints.

[0158] Further, commercially available products were used to automate the sample preparation procedure used for CLAUDIA, which was also performed manually (i.e., serum separation, liquidliquid extraction, filtering, and diluting steps), to process the blood samples to get a clean sample that can go on the HPLC-MS. To achieve this, a first sample preparation module (e.g., an automated liquid handler) was used. The first sample preparation module was equipped with anEMR-lipid filtration plate or device for the filtration step, which combines a depth filter for mechanical filtration with a special component that removes lipids and cellular debris from the samples. After the sample was processed in this manner, the samples were loaded onto the HPLC- MS, which directly integrated with the first ample preparation module. Further, the first sample preparation module served as the first version of automating this system, but, in parallel, Agilent’s facilities and expertise were utilized to perform all the sample preparation steps without the first sample preparation module, and instead only using an Online Sampler on the Agilent 1260 HPLC system (z.e., a second sample preparation module). An advantage of this system over the first sample preparation module is that this method eliminated the footprint of the first sample preparation module, which enabled CLAUDIA to take up less space clinically, which will increase the likelihood of clinical acceptance of this system. Another advantage of this system is the Online Sampler moved faster than the sample preparation module, which enabled CLAUDIA to sample drug concentrations faster, thus allowing drug concentrations to be sampled more rapidly than previously possible. In both systems, the HPLC System was coupled to and processed by monitoring software (e.g., Agilent’s Online system and OpenLab CDS Online LC Monitoring Software), which enabled automatic, near real-time data analysis.

[0159] Previous experiments, which used a PID controller with an adaptive tuning algorithm, demonstrated the need for using an adaptive tuning element to enable the control of the drug concentration over a range of clinically relevant conditions. Correspondingly, a more advanced adaptive tuning algorithm was developed. Specifically, a rabbit’s PK was modeled as a first order system, which is a good model of the PK of 5-FU in rabbits. A least squares estimator was used to estimate the parameters (i.e., time constant and gain) of the first order system given the concentration-time data from the experiment. The updated model of the rabbit’s PK was used to calculate the controller parameters (i.e., the proportional and integral controller gains) after every additional timepoint. Controller parameters were updated 32-minutes after the start of the experiment to enable sufficient datapoints to make an accurate estimate of the rabbit’s PK given the noise in the system. Given the ability of this adaptive tuning system to precisely predict the rabbit’s PK and thereby select correct gains, a PI controller was selected, which is less sensitive to noise in the system and has been shown in preliminary simulations to perform well under a range of conditions.

[0160] Once the different components of the system were developed, they were combined into a fully integrated system to enable translation of CLAUDIA to the clinic, as illustrated in FIG. 23. The system can be fully automated by connecting the different components using the Agilent Online LC system software, which can coordinate the individual components to handle the samples at specific times that are required. Thus, consistent, robust measurements can be taken from the patient, enabling an accurate determination of the updated infusion rate that should be used, which this software also controlled.

[0161] In some examples, monitoring software (e.g., Agilent Online LC Monitoring Software) can be adapted to integrate all components of CLAUDIA into a fully autonomous and integrated system (e.g., procedures used by Agilent for automated sampling from a batch reactor). In that system, an isocratic pump, a piston pump, a peristaltic pump, and a programmable syringe pump were all controlled by the Agilent Online LC Monitoring Software to sample a reaction mixture and place the reaction mixture onto a HPLC-MS to get a real time concentration measurement. This demonstrates the ability of this software to control multiple different pumps, which can be leveraged to automate CLAUDIA. Further, Online LC software can be used to control the pumps for the blood drawing from the patient. The software can trigger the peristaltic pump to draw blood from the patient and place the blood sample in a vial on the sample preparation system that will then process the blood sample. The software can then turn off this pump and activate the syringe pump to flush the line with saline, and then place this component of the system on standby (i.e., the tube will remain filled with saline) until the next blood sample is needed. Once the blood reaches the HPLC-MS sample vial, the sample preparation process discussed above can be activated to process the sample and then analyze the concentration of drug via HPLC-MS. The Online LC Monitoring System can automatically determine the concentration of the drug from the HPLC-MS chromatogram. The Agilent Software can edit the Online LC code to take the calculated concentration of the drug and input it into a control algorithm (e.g., the control algorithm(s) discussed above), which can be implemented directly in the Online LC Monitoring System. The Online LC system can then change the infusion rate of a syringe pump that will infuse the drug into the patient. This process can be repeated every timepoint for the duration of the treatment of the patient.

[0162] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable mediumencoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0163] As used in the claims, the phrase "at least one of A, B, and C" means at least one of A, at least one of B, and / or at least one of C, or any one of A, B, or C or combination of A, B, or C. A, B, and C are elements of a list, and A, B, and C may be anything contained in the Specification.

[0164] The present invention has been described in terms of one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

Claims

WHAT IS CLAIMED IS:

1. A drug-delivery method for adjusting an infusion rate of a drug being delivered to a patient, the drug-delivery method comprising: extracting a fluid sample from the patient; isolating a drug filtrate by processing the fluid sample within a first time period; analyzing the drug filtrate for a drug concentration using a chromatography system, a mass spectrometry system, or both within a second time period; comparing, using a control process, the drug concentration with a target concentration range; and adjusting the infusion rate of the drug to ensure the drug concentration is within the target concentration range.

2. The method of claim 1, wherein the first time period is less than about 180 seconds.

3. The method of claim 1, wherein the second time period is less than about 300 seconds.

4. The method of claim 1, wherein processing the drug filtrate includes: centrifuging the fluid sample for about thirty seconds to isolate blood plasma; mixing the blood plasma directly with an extraction solvent to perform liquid-liquid extraction of a first supernatant; vortexing the first supernatant for about five seconds to isolate a second supernatant; centrifuging the second supernatant for about thirty seconds to isolate a third supernatant; and filtering the third supernatant through a syringe filter to isolate the drug filtrate.

5. The method of claim 1, wherein processing the fluid sample includes: mixing the fluid sample directly with an extraction solvent to perform liquid-liquid extraction of a first supernatant; vortexing the first supernatant for about five seconds to create a mixture of blood and solvent; centrifuging the mixture of blood and solvent for about 30 seconds to isolate a third supernatant; and filtering the third supernatant through a filter to isolate the drug filtrate.

6. The method of claim 1, wherein analyzing the drug filtrate includes passing the drug filtrate through a liquid chromatography column to perform an isocratic elution of the drug filtrate.

7. The method of claim 6, wherein the liquid chromatography column is a hydrophilic interaction liquid chromatography column or a Cl 8 high performance liquid chromatography HPLC column.

8. The method of claim 1, wherein the control process is controlled by a controller having a plurality of controller gains.

9. The method of claim 8, wherein the controller is a proportional controller, an integral controller, a derivative controller, or any combination thereof.

10. The method of claim 8, wherein the control process is an adaptive tuning process.

11. The method of claim 1, wherein the infusion rate is adjusted according to a chronomodulated profile.

12. The method of claim 1, the method further comprising: adjusting one or more subsequent infusion rates for one or more subsequent drugs being delivered to the patient.

13. The method of claim 12, wherein adjusting the one or more subsequent infusion rates is dependent on adjusting the infusion rate of the drug.

14. A drug-delivery system for adjusting an infusion rate of a drug being delivered to a patient comprising: a fluid transport system having at least one inlet and at least one outlet; a pump coupled to the fluid transport system, the pump being configured to draw a fluid sample from the patient through a first inlet of the fluid transport system; a sample preparation system coupled to a first outlet of the fluid transport system, the sample preparation system being configured to receive the fluid sample and isolate a drug filtrate therefrom in a first time period; an analyzer including a liquid chromatography column coupled to a mass spectrometer, the analyzer being configured to analyze the drug filtrate for a drug concentration in a second time period; and a controller coupled to the analyzer, the controller being configured to control a control process to compare the drug concentration with a target concentration range, wherein the controller is further configured to adjust the infusion rate of the drug through the fluid transportation system to ensure the drug concentration is within the target concentration range.

15. The drug-delivery system of claim 14, wherein the sample preparation system includes at least one of a centrifuge, a microfluidic mixer, and a vortex mixer, or a syringe filter.

16. The drug-delivery system of claim 14, wherein the liquid chromatography column is a C 18 high performance liquid chromatography (HPLC) column, a hydrophilic interaction liquid chromatography column, or both, and wherein the mass spectrometer is a time of flight mass spectrometer.

17. The drug-delivery system of claim 14, wherein the first time period is less than about 180 seconds.

18. The drug-delivery system of claim 14, wherein the second time period is less than about 300 seconds.

19. The drug-delivery system of claim 14, wherein the controller has a plurality of controller gains and is a proportional controller, an integral controller, a derivative controller, or any combination thereof to perform an adaptive control process.

20. The drug-delivery system of claim 14, wherein the controller is further configured to control the fluid transport system to use a continuous rate infusion profile, a step-function infusion profile, a trapezoidal infusion profile, a triangular infusion profile, or any combination thereof to deliver the drug to the patient.

21. The drug-delivery system of claim 14, wherein the sample preparation system includes a mixer defining a mixing channel that extends between at least one inlet and an outlet, the mixing channel including a plurality of turns.

22. The drug-delivery system of claim 14, wherein the controller is further configured to automate at least one of the pump to draw the fluid sample from the patient; the sample preparation isolate the drug filtrate from the fluid sample; or the analyzer to analyze the drug filtrate for the drug concentration.

23. A drug-delivery method to target a specific serum concentration range in a patient using a drug-delivery system comprising: accessing a blood or serum from the patient; measuring an amount of drug in the blood or serum accessed from the patient; and controlling the drug-delivery system based on the amount of a drug in the blood or serum to target the specific serum concentration range in the patient.

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