GB2564843A - Addressable systems for monitoring metabolic pathways - Google Patents

Abstract

Described is an enzyme electrode system for monitoring multiple metabolites in a sample. The enzymes are encapsulated in polymeric capsules, crosslinked polymers, sol-gels or metal-organic frameworks (MOFs). The enzymes are individually addressable via conducting composites, allowing signals from different enzymes to be distinguished at different voltages. The composites may be pyrrole-polyoxometalate or poly 3,4-ethylenedioxythiophene (PEDOT)-platinum nanoparticles. The electrode may be a carbon electrode. The monitored metabolites may be monosaccharides such as glucose, lactate, beta-hydroxybutyrate, alpha-hydroxy acid amino acids or branched chain amino acids such as leucine. Also described are uses of the system in methods of monitoring diseases such as diabetes.

Description

ADDRESSABLE SYSTEMS FOR MONITORING METABOLIC PATHWAYS

BACKGROUND [02] Existing technology for monitoring single individual metabolites in the home or away from the laboratory is focused on electrochemical biosensors such as blood glucose sensors (for example, Accu-Chek™ from Roche; Contour™ from Ascensia Diabetes Care; OneTouch™ from LifeScan; and Freestyle™ from Abbott), lactate sensors (for example, Lactate Scout+™ from EKF) and β-hydroxybutyrate sensors (for example, Precision Xtra™ from Abbott) that are available on the market. However, these instruments are not suitable for simultaneous and collective data analysis of multiple metabolite measurements to generate meaningful data reflecting the detailed pathophysiology of patients. Expensive, high-performance clinical laboratory instruments are available, but lack the mobility to deliver relevant real-time measurement.

[03] Designs of analytical instruments are moving towards the use of miniaturised encapsulated catalytic system, such as microparticles and microemulsions as highperformance and cost-effective platforms for various applications such as medical diagnostics and biological assays. Successful examples of this trend are: the Luminex® technology which uses microparticles for rapid and high-throughput affinity (immuno and geno) assays, where tens of thousands of parallel assays take place, for each sample, within a suspension of individual particles. Another alternative design for ultrasensitive, high-throughput affinity assay is the Quanterix® system, where individual microparticles are immobilised into addressable femtolitre-sized wells; this approach has been shown to allow up to 1000 times greater sensitivity over conventional assays with CVs <10%. Recently the Digital-PCR® system, introduced the concept of micro-compartments in combination with high-throughput analysis; in this technology microemulsion droplets are used to encapsulate PCR reagent mixture creating individual PCR microreactors for highly sensitive DNA detection (down to a single DNA copy).

[04] Microparticles and microemulsions are useful platforms for high performance analytical assays. However, the microparticle platform is limited to surface binding assays, while microemulsions lack permeability for substrate or analyte diffusion. Hollow polymer microcapsules created via Layer-by-Layer (LbL) techniques to deliver thin-film architectures of colloidal materials are promising for biomedical applications, especially for microencapsulation and controlled release. These polymer capsules are simply fabricated by sequential deposition of multiple layers of alternating positively/negatively changed polyelectrolytes onto sacrificial template particles, followed by removal of the template (Caruso 2003).

[05] Recent research exploiting the advantages of the LbL microencapsulation system, has been extended to the use of microcapsules to mimic cellular systems and to perform biochemical reactions (Mak et. al. 2008). Microcapsules have been fabricated using the layerby-layer (LbL) approach with deposition of a polyelectrolyte multilayer. The overall capsule wall thickness and permeability can be precisely controlled by altering the deposition conditions (for example, ionic strength, pH and layer number) (Caruso 2000) and even a wall comprising a single layer can be produced. Other functions such as magnetic and optical properties can be introduced into the capsule membrane by simply incorporating magnetic or plasmonic nanoparticles into the multilayer for the creation of smart capsules (Peyratout et. al. 2004; Fang et. al. 2002). These smart capsules have been integrated with microfluidic and lab-on-a-chip systems for optical sensing. However, no way has previously been found to integrated addressable microcapsules within simple electrochemical systems to meet the considerable commercial need for multianalyte sensors for home diagnostics.

[06] No previous technology has addressed the urgent and unmet need for home or decentralised monitoring of metabolic diseases or disturbance from a holistic perspective. It is increasingly realised that personalised treatment tailored to the individual can lead to far better outcomes. In the case of diseases such as diabetes, commonly only one metabolite is monitored, and that is glucose. This is monitored either in blood as a one-shot measurement or continuously via subcutaneously implanted sensors. Gylcosylated haemoglobin is also commonly used as an integrated measurement of glucose level in the body over extended periods of time. At best, a second metabolite, β-hydroxybutyrate, may be monitored intermittently under certain pathophysiological conditions. No device or method is readily available to measure multiple metabolites simultaneously and to match these metabolic patterns to the control of an individual’s disease or to improve their health or physical performance. This invention addresses this need and provides the basis for the development of a novel range of commercial products to treat disease and to help maintain health.

SUMMARY [07] This invention provides a device and method for simultaneously monitoring multiple metabolic pathways, using addressable and modular combinations of encapsulated catalytic systems, and the generation of metabolite patterns as a new diagnostic parameter. The metabolite pattern generated describes the relationship between the selected multiple metabolites and reflects the metabolic status of the user.

[08] The invention also describes the creation of encapsulated catalytic systems that entrap different enzymes, particularly oxidoreductases, for monitoring different metabolic pathways.

Examples include but are not restricted to: encapsulated glucose oxidase or glucose dehydrogenase systems to monitor the carbohydrate metabolic pathway; encapsulated βhydroxybutyrate systems to follow the fat metabolic pathway; encapsulated amino acid oxidase, leucine dehydrogenase or leucine transaminase systems to follow the amino acid metabolic pathway; and encapsulated lactate oxidase or lactate dehydrogenase to reflect the metabolism of insulin.

FIGURES [09] Figure 1 shows the scanning electron microscopic image of an encapsulated catalytic system composed of PEDOT-platinum nanoparticle-glucose oxidase.

[10] Figure 2 illustrates the application of the addressable and modular combinations of separately immobilised encapsulated catalytic systems for simultaneous detection of glucose and lactate.

[11] Figure 3 shows the simultaneous signal response and pattern of the signal response of the addressable and modular combinations of encapsulated catalytic systems for glucose and lactate detection.

[12] Figure 4 shows the signal response and pattern of the signal response of two different addressable encapsulated catalytic systems with different intrinsic electrochemical signatures such as pyrrole-polyoxometalate-glucose oxide and PEDOT-platinum nanoparticle-lactate oxidase immobilised at the same physical position on an electrode and which allows for simultaneous detection of glucose and lactate.[13] Figure 5 shows a decision flow diagram for the interpretation of the simultaneous signal responses and patterns of multiple metabolites followed by an output in the form of a recommended action.

DETAILED DESCRIPTION OF THE INVENTION [14] Encapsulated catalytic systems provide a way of immobilising biocatalytic or catalytic molecules, while remaining permeable to metabolite diffusion. Examples of encapsulated catalytic systems include but are not restricted to polymeric capsules, crosslinked polymers, solgels and metal organic frameworks-MOFs. By selection of the encapsulated enzyme, the encapsulated catalytic system can be made specific for a particular metabolite such as glucose, β-hydroxy butyrate or leucine; or can be broadly specific a class of collective metabolites such as monosaccharides, ketones, amino acids or branched chain amino acids.

[15] The construction of electrochemical biosensors for monitoring multiple metabolic pathways simultaneously, comprises at least two or more of the modularly encapsulated catalytic systems immobilised onto electrodes with different physical addresses or positions. In addition, the encapsulated catalytic systems for different metabolic pathways can be addressed with intrinsic electrochemical signatures, for example, by being composed of different conducting composites that respond to different applied working potentials, such that individual potential channels correspond to a readout from different encapsulated catalytic systems.

[16] In another embodiment, the physical address and/or the intrinsic electrochemical signatures raised from the modularly encapsulated catalytic systems reduce cross talk between different encapsulated catalytic systems for multiple metabolite detection.

[17] The collective signal response generated from the different encapsulated catalytic systems representing different pathways is processed to generate a pattern of response rather than individual values, as in conventional systems. These algorithms present higher order values or recommendations to the user based on the synthesis of two or more values.

[18] The formation of encapsulated catalytic system in the form of polymeric capsules is achieved, for example, by using a Layer-by-Layer (LbL) technique, during which multiple polyelectrolyte layers are coated onto the surface of enzyme loaded templates via a step-wise deposition process. By using the Layer-by-Layer technique, the permeability of the capsule wall can be controlled by the number of polyelectrolyte layers, the nature of the polyelectrolyte materials, the molecular weight of the polyelectrolyte and the degree of crosslinking. The formation of encapsulated catalytic systems in the form of polymeric capsules can also be achieved by using interfacial polymerisation techniques at the surface of the enzyme loaded template. The permeability of the capsule wall can be controlled by the monomer materials, the molecular weight of monomers, the polymerisation time and the catalyst materials. The formation of encapsulated catalytic systems in the form of crosslinked polymers can be achieved by template-assisted polymerisation or emulsion polymerisation. The immobilisation of the enzymes can be achieved by pre-loading of the enzymes into the template or emulsion droplet. Alternatively, the immobilisation of the enzymes can be achieved by post-loading of the enzymes to the crosslinked polymer.

[19] In one embodiment, the crosslinked polymer and polymeric capsule are composed of conductive polymers. Examples are polypyrroles, polyanilines, poly(thiophene)s and their derivatives.

[20] In another embodiment, the encapsulated catalytic system is formulated into functionalised inks. Different formats are possible to create a hierarchical biosensor interface by bulk deposition of the mixed functionalised ink, or by using layered deposition approaches. The encapsulated catalytic systems serve as building blocks, creating multilevel hierarchical structured biosensor surfaces. This versatile approach allows functional biosensing interfaces to be created with the advantages of increased active surface area for interfacial biocatalytic reactions and an intrinsic hierarchical structure facilitating the diffusion of metabolites to the encapsulation catalytic system.

[21] In another embodiment, the encapsulated catalytic system provides for the compartmentalisation of different enzymes to reduce cross talk between different encapsulated enzyme systems for the multiple metabolite detection.

[22] One of many examples where this approach is useful is for simultaneous multiparameter monitoring to improve the management of diabetes. The main goal in treating individuals with diabetes is to reduce the risk of short-term and long-term complications. With this aim, clinicians strive for near-normal glycaemia with as little glucose variability as possible. Currently, various technical devices are available to assist patients and diabetes specialists in achieving optimal diabetes control. Despite these technical achievements, diabetes control faces a number of challenges including crucially the high inter- and intra- subject variability, which highlights the need for personalised insulin treatment schemes for each diabetic individual. Furthermore, effective metabolic control is highly dependent on the accurate estimation of effects due to disturbances, such as meals and physical activity, which is usually poor or even impossible, leading to inadequate prevention of both hyper- and hypoglycemic events.

[23] Advances in information technology have allowed the development of a broad spectrum of multiple metabolite algorithmic approaches towards enhanced self-management of diabetes and a better understanding of the underlying disease mechanisms.

[24] In one example, a multiple metabolite sensor for diabetes was constructed, where the encapsulated catalytic systems measure metabolites associated with diabetes by combining, for example, a: poly 3,4-ethylenedioxythiophene (PEDOT) encapsulated glucose dehydrogenase system; PEDOT encapsulated β-hydroxybutyrate dehydrogenase system; PEDOT encapsulated leucine dehydrogenase system; and PEDOT encapsulated lactate dehydrogenase system. The electrochemical biosensor for monitoring multiple metabolic pathways simultaneously, was constructed by immobilising the modularly encapsulated catalytic systems onto carbon electrodes with different physical addresses or positions. These dehydrogenases catalyse the oxidation of the respective analytes via a reduction reaction and transfer hydrides (H-) to the electron acceptor (i.e. NAD⁺), forming a NADH, which is detected by electrochemical techniques such as amperometry, potentiometry or other electrochemical techniques. The signal outputs from different encapsulated catalytic systems is proportional to the amount of the corresponding metabolite in the sample. However, in this device, the individual numerical values are combined using algorithms to deliver useful information to the lay user regarding their personal metabolic status.

[25] An example of addressing the same physical position on an electrode is using encapsulated catalytic systems composed of different conducting composites such as one encapsulated catalytic system composed of PEDOT-platinum nanoparticle-lactate oxidase and another encapsulated catalytic system composed of pyrrole-polyoxometalate-glucose oxidase, which respond to different applied working potentials on the same electrode.

[26] Figure 1 shows the scanning electron microscope (SEM) image of an example of encapsulated catalytic systems composed of PEDOT-platinum nanoparticle-glucose oxidase. The fabrication of the PEDOT-platinum nanoparticle-glucose oxidase encapsulated catalytic system is based on a removable template- (such as calcium carbonate) polymerisation method. In brief, 3,4-ethylenedioxythiophene (EDOT) was infused into the calcium carbonate template. Then, the EDOT-loaded calcium carbonate template particles were washed with propanol to remove access EDOT. The template-assisted polymerisation of EDOT was started by addition of an initiator (such as copper perchlorate, ferric chloride etc.). After the polymerisation reaction was completed, the calcium carbonate templates were removed by addition of a chelating agent (such as ethylenediaminetetracetic acid) or by addition of acids, thus yielding the PEDOT particles. The PEDOT particles were then mixed with a platinum ion solution followed by the addition of a reducing reagent (such as sodium borohydride) and resulting in the in-situ formation of platinum nanoparticles within the PEDOT particles, thus creating a PEDOTplatinum nanoparticle composite. Finally, enzyme solution (such as glucose oxidase) was loaded into the PEDOT-platinum nanoparticle composite by physical and/or chemical immobilisation, to yield a PEDOT-platinum nanoparticle-glucose oxidase encapsulated catalytic system.

[27] Figure 2 shows an example of using the addressable and modular combinations of separately immobilised encapsulated catalytic systems for simultaneous detection of multiple metabolites. The electrode comprises two different modularly encapsulated catalytic systems immobilised onto electrodes with different physical addresses and/or positions. Figure 2(1) is a counter electrode composed of carbon. Figure 2(2a) is a working electrode composed of the first encapsulated catalytic system (i.e. the PEDOT-platinum nanoparticle-glucose oxidase) and Figure 2(2b) is a working electrode composed of the second encapsulated catalytic system (i.e. the PEDOT-platinum nanoparticle-lactate oxidase). Figure 2(3) is a reference electrode, such as silver/silver chloride. Figure 2(4) is a conductive track made of, for example, carbon or silver impregnated carbon). Figure 2(5) is a substrate base, such as ceramic, on which the structure is supported. Figure 2(6) is an insulating layer. During the multiple metabolite detection, both working electrodes (2a) and (2b) operated at the same time for simultaneous detection glucose and lactate within a single sample. In one embodiment, individual modules of the encapsulated catalytic systems (i.e. working electrode (2a) and (2b) can be selectively switched on and/or switched off, by controlling the electrical circuit and/or the applied potential.

[28] Figure 3 shows the simultaneous signal response and pattern of the signal response of the addressable and modular combinations of encapsulated catalytic systems composed of PEDOT-platinum nanoparticle-glucose oxidase and the PEDOT-platinum nanoparticle-lactate oxidase with the electrochemical detection technique. Figure 3A-1 shows the PEDOT-platinum nanoparticle-glucose oxidase channel for the detection of glucose. Figure 3A-2 shows the PEDOT-platinum nanoparticle-lactate oxidase channel for the detection of lactate. Figure 3A3 shows the signal response and pattern of the signal response for the simultaneous detection of sample contains glucose (1 mM) and lactate (1 mM). Figure 3B-4 shows the PEDOTplatinum nanoparticle-glucose oxidase channel for the detection of glucose. Figure 3B-5 shows the PEDOT-platinum nanoparticle-lactate oxidase channel for the detection of lactate. Figure 3A-6 shows the signal response and pattern of the signal response for the detection of sample contains only glucose (1 mM). Figure 3B-7 shows the PEDOT-platinum nanoparticle-glucose oxidase channel for the detection of glucose. Figure 3B-8 shows the PEDOT-platinum nanoparticle-lactate oxidase channel for the detection of lactate. Figure 3A-9 shows the signal response and pattern of the signal response for the detection of sample contains only lactate (1 mM).

[29] Figure 4 shows the simultaneous signal response and pattern of the signal response of two different addressable encapsulated catalytic systems such as pyrrole-polyoxometalateglucose oxide and PEDOT-platinum nanoparticle-lactate oxidase immobilized at the same physical position on an electrode. Figure 4-1 and Figure 4-2 shows the encapsulated catalytic system composed of pyrrole-polyoxometalate-glucose oxidase for the detection of glucose with intrinsic electrochemical signatures at around -0.25 volts (Figure 4-1 before addition of glucose and Figure 4-2 after addition of 1 mM glucose). Figure 4-3 and Figure 4-4 shows the encapsulated catalytic system composed of PEDOT-platinum nanoparticle-lactate oxidase for the detection of lactate with intrinsic electrochemical signatures at around 0.6 volt (Figure 4-3 before addition of lactate and Figure 4-4 after addition of 1 mM lactate).

[30] Figure 5 shows an example of a decision flow diagram for the interpretation of the simultaneous signal response and pattern of multiple metabolites which describes the relationship between the selected multiple metabolites (such as glucose and lactate). By defining the threshold values (i.e. threshold concentration) for different metabolites and calculating and/or processing the relationship between the selected multiple metabolites, different sets of actions (i.e. recommendation) are provided as an output signal. The user may, for example, receive output in the form of a traffic light system where a green signal indicates that no action is required, a red signal indicates that immediate action is required while an amber light suggest that a proactive adjustment to a patient’s regimen be adopted. These indicative colours can be displayed electronically on for example a mobile phone or computer where they can be further interrogated via an interactive programme to yield further levels of more detailed information or recommendations.

[31] Profound metabolic changes occur in people with Type 1 diabetes mellitus during insulin deprivation. These include an increase in basal energy expenditure and reduced mitochondrial function. In addition, protein metabolism is significantly affected during insulin deprivation. A greater increase in whole-body protein breakdown than protein synthesis occurs resulting in a net protein loss. During insulin deprivation, the splanchnic bed has a net protein accretion which accounts for the total increase in whole-body protein synthesis while muscle is in a net catabolic state (Hebert et. al, 2010). Issues with exogenous insulin therapy (e.g. glycaemic variability, hypoglycaemia, insulin resistance and weight gain) continue to prevent patients with Type 1 diabetes from achieving adequate glycaemic control. Recent data show that 70-86% of adults with Type 1 diabetes in the USA do not achieve target HbA1c <7% (<53 mmol/mol) (Miller et. al, 2015). Added to this are distinct ethnic and genetic differences in insulin sensitivity.

[32] Lactate, formerly considered simply as a waste product of glycolysis, has drawn increasing attention as a crucial regulator of insulin resistance and diabetes mellitus. Over recent decades, experiments have revealed that lactate is both a powerful fuel and signaling molecule, and it is continuously being produced and circulated through the body (Gladden et. al., 2004). Glycolysis normally results in the production of pyruvate which can then be used further in the body for gluconeogenesis, the citric acid cycle and other pathways. If a lot of energy is needed or catabolism is elevated, pyruvate is converted by pyruvate dehydrogenase into acetyl-CoA. The problem with diabetes is that the pyruvate dehydrogenase can be inhibited. If the body then needs a lot of energy pyruvate will be converted into lactate which is released by the cells into the bloodstream. Gluconeogenesis cannot be activated since this needs either pyruvate, acetyl-CoA or oxaloacetate as starting material. The other problem with this process is that this happens in the liver and when the glucose is released into the bloodstream, it cannot be taken up by the cells due to the lack of insulin.

[33] New diabetic medications such as the sodium glucose co-transporter 2 selective inhibitors (e.g. Ipragliflozin and Dapagliflozin) decrease reabsorption of glucose in the kidneys. While the risk of hypoglycaemia is low, there is a risk of diabetic ketoacidosis with this medication both in Type 1 and Type 2 diabetes. When β-hydroxybutyrate is measured simultaneously with glucose, the medication is found to increase β-hydroxybutyrate. Hence, patients with Type 1 diabetes could develop ketoacidosis (Erondu et. al., 2015).

[34] During insulin deprivation in subjects with Type 1 diabetes there is an increase in circulating amino acids (particularly branched chain amino acids like leucin) and ketones. Branched chain amino acids increase leucine oxidation and whole-body protein synthesis and inhibit protein breakdown. Amino acids have also been shown to be a key regulator of protein synthesis in the splanchnic bed. It has been proposed that amino acids are released from the breakdown of skeletal muscle protein during insulin deprivation. Glucagon, the hormone determined to be largely responsible for the increased energy expenditure during insulin deprivation in Type 1 diabetic individuals, also increases leucine.

[35] Many patients with Type 2 diabetes will normalise the blood sugar levels on losing weight with or without medication, but it is very difficult to maintain this remission without a gastric bypass. This indicates that intensive care with comprehensive self-monitoring of glucose and other metabolites should be lifelong in this case too.

[36] The precise ratios of these key metabolites vary in individual cases and circumstances and can be used to provide superior treatment regimens and to provide early warnings of unexpected excursions during therapy. The device described in this invention processes the simultaneous measurement of each of these individual parameters to present a unique synthesis of information to reflect an individual’s particular condition to improve personalised management and to deliver straightforward early warnings of potentially dangerous disturbances that could not be foreseen by individual measurements alone. For example, a pattern of normal glucose, lactate and amino acids, but elevated β-hydroxybutyrate during treatment with Ipragliflozin would suggest the need to adjust medication despite near norm glycaemia. Furthermore, an individual can use the device to personalise their treatment plan by using the built-in learning algorithms to correlate their individual metabolic state to observable indicators, such as feelings of wellbeing and longer-term indicators such as glycosylated haemoglobin.

Insulin deprivation results in an increase in:

1) Glucose [normal range 4.0 to 6.0 mmol/L]

2) Circulating amino acids (particularly branched chain amino acids) [normal range for leucine 66 to 170 pmol/L]

3) Ketones (β-hydroxybutyrate) [normal level < 0.6 mmol/L. Between 0.6 and 1.5 mmol/L recheck blood glucose and ketones in 2-4 hours. Between 1.5 and 3.0 mmol/L may be at risk for developing ketoacidosis.]

4) Lactate [normal range in unstressed patients is 0.5-1 mmol/L. Patients with critical illness can be considered to have normal lactate concentrations of less than 2 mmol/L. Hyperlactatemia is defined as a persistent, mild to moderate (2-4 mmol/L) increase in blood lactate concentration without metabolic acidosis, whereas lactic acidosis is characterised by persistently increased blood lactate levels (usually >5 mmol/L) in association with metabolic acidosis.] [37] If one simply considers a normal or elevated level for each of 4 parameters then 48 permutations exist. If this is expanded to include 2 or three ranges for each of multiple analytes then the decision process becomes impossible without a look-up table or other algorithm. Add to this individual and temporal variation and the utility of our invention in tracking the metabolism of a particular individual becomes even more apparent. The point is illustrated below in the simplest of possible matrices where elevation of just one parameter at a time is condidered:

1) All parameters within acceptable clinical range = GREEN i.e. NO ACTION

2) All parameters elevated above normal range = RED i.e. ACTION - seek medical advice

3) β-hydroxybutyrate slightly elevated but all others normal = AMBER i.e. PROACT adjust Ipragliflozin medication

4) Glucose slightly elevated but other levels normal = AMBER i.e. PROACT repeat measurement later

5) Glucose slightly depressed, β-hydroxybutyrate elevated = AMBER i.e. PROACT adjust insulin dose

6) Glucose depressed, all other levels elevated = RED i.e. ACTION seek medical advice [38] The readout may be delivered via an app on. For example, a mobile phone. The three colour initial feedback may be displayed on a touch sensitive screen and each dot may be interrogated further by touching it to reveal more detailed information and or recommendations.

[39] Still further non-limiting examples include multiple metabolite sensors for obesity, diabetes support systems targeting different metabolites such as acetylcholine (with an encapsulated acetylcholinesterase system), leucine (with encapsulated leucine dehydrogenase system and xanthine (with encapsulated xanthine oxidase system). More examples are: genetic metabolic disorders, sports medicine and endurance monitoring, where an understanding of the simultaneous interplay of the metabolic system is critical for managing the health or performance of the individual.

[40] The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the claims.

LITERATURE CITED IN THE DESCRIPTION

Caruso F. (2003). Hollow inorganic capsules via colloid-templated layer-by-layer electrostatic assembly. Colloidal Chemistry II Book Series: TOPICS IN CURRENT CHEMISTRY Volume: 227 Pages: 145-168

Mak W.C., Cheung W.Y., Trau D. (2008). Diffusion controlled and temperature stable microcapsule reaction compartments for high throughput microcapsule-PCR. Advanced Functional Materials, 18, 2930-2937.

Caruso F. (2000). Hollow Capsule Processing through Colloidal Templating and SelfAssembly, Chemistry - A European Journal Chemistry, 6, 413-419.

Peyratout L., Dahne L. (2004). Tailor-Made Polyelectrolyte Microcapsules: From Multilayers to Smart Containers. Angew. Chemie., 43, 3762-3783.

Fang M., Grant P.S. McShane M.J., Sukhorukov G.B., Golub V.O., Lvov Y.M. (2002). Magnetic bio/nanoreactor with multilayer shells of glucose oxidase and inorganic nanoparticles. Langmuir, 18, 6338-6344.

Hebert S.L., Nair K.S. (2010) Protein and energy metabolism in type 1 diabetes. Clin. Nutr., 29(1), 13-17.

Miller K.M., Foster N.C., Beck R.W., Bergenstal R.M., DuBose S.N., DiMeglio L.A., Maahs D.M., Tamborlane W.V., T1D Exchange Clinic Network. (2015). Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry. Diabetes Care, 38(6), 971-978.

Gladden L.B., (2004). Lactate metabolism: a new paradigm for the third millennium. Journal of Physiology, 558(1), 5-30.

Erondu N., Desai M., Ways K., MeiningerG. (2015). Diabetic Ketoacidosis and Related Events in the Canagliflozin Type 2 Diabetes Clinical Program. Diabetes Care, 38(9), 16801686.

Claims (13)

1. A method or device to simultaneously monitor two or more metabolites in a sample, using combinations of encapsulated oxidoreductase enzymes intrinsically addressable via conducting composites that respond to different applied working potentials in an electrochemical measurement system.

2. A method or device according to the Claim 1 in which the enzymes are encapsulated in polymeric capsules, crosslinked polymers, sol gels or metal organic frameworks-MOFs.

3. A method or device according to any of the previous claims where the encapsulated systems are deposited on electrodes.

4. A method or device according to any of the preceding Claims where the metabolites monitored are a monosaccharide, lactate, β-hydroxybutyrate, and an alpha-hydroxy acid amino acid or a branched chain amino acid.

5. A method or device according to any of the preceding Claims where the metabolites monitored are glucose, lactate, β-hydroxybutyrate and leucine.

6. A method or device according to any of the previous Claims in which the encapsulated catalytic system is formulated into a functionalised ink to facilitate printing.

7. A device according to any of the previous Claims in which the individual electrochemical signals are displayed as a single value or signal providing a diagnostic parameter reflecting a specific pattern of metabolites.

8. A device according to the Claim 7 where the diagnostic parameter is displayed in the form of one or more coloured lights.

9. A device according to the Claim 7 where the diagnostic parameter may be transmitted or further processed via a telecommunication system.

10. A method or device according to any of the previous Claims whereby automatic decisions are facilitated or actuated based on the pattern of response.

11. A method or device according to Claim 7 where the diagnostic parameters are used to manage a metabolic disease by adjusting diet, lifestyle or medication.

12. A method or device according to any previous Claims used to manage diabetes.

13. A method or device according to any previous Claims used to manage obesity, genetic metabolic diseases, sports performance or endurance.