Delivering precision antimicrobial therapy through closed-loop control systems

Delivering precision antimicrobial therapy through closed-loop control systems Abstract Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients. Introduction Antimicrobial resistance (AMR) threatens to be a leading cause of death by 20501 making it a global patient safety issue. A major driver of AMR is the inappropriate use of antimicrobials in humans and animals.2 To date, research in this field has focused on optimizing the selection of antimicrobial agents. However, these strategies often fail to also consider optimization of the dose of the antimicrobial agent, which should aim to be sufficient to maximize bacterial killing whilst negating the harmful consequences of therapy, such as development of AMR and toxicity to the host. Data are emerging within certain patient populations, such as critically ill patients, describing wide variations in how individuals handle antimicrobials (pharmacokinetics; PK).3–7 These wide variations in individual PK appear to be associated with increased variation in the effects of therapy, including outcomes of treatment and the development of AMR (their pharmacodynamics; PD).3–7 In response to the observed variations in individual PK, there has been a shift in the focus of therapeutic drug monitoring (TDM) away from primarily being used to prevent toxicity caused by antimicrobials with narrow therapeutic windows, towards enhancing the efficacy of less toxic agents such as the β-lactams, in order to optimize the outcomes of treatment.4,8–13 However, to achieve true individualization of therapy, we require a focus on not just the PK of antimicrobial agents. We must also understand the individual patient’s physiology as well as the characteristics of the organism that we are treating. One method that has been explored widely is the use of Bayesian dose optimization platforms.3 Whilst TDM linked with Bayesian forecasting provides a powerful opportunity for delivering individualized care for patients,3,14 several gaps in current strategies for dose optimization of antimicrobials have hindered clinical implementation. Most notably, methods for more-continuous monitoring to allow real-time adaptive dosing of agents are still not available. Other challenges include difficulties in access to appropriate antimicrobial assays,12,15–20 poor integration of dosing software with electronic health records and decision support systems,3,21 challenges with collecting and handling PK samples,22,23 and failures of compliance with PK sampling protocols currently being used by healthcare professionals.24 Validation of novel methods for the monitoring and dose optimization of antimicrobial agents is required. Whilst several studies have explored the role of microfluidics,22,23,25 these are still hindered by many of the problems associated with routine antimicrobial TDM strategies, such as the need for laboratory analysis and transport of blood products. One potential method for avoiding these problems is the development of closed-loop systems based on minimally invasive, microneedle electrochemical sensor technology.26 This technology has been demonstrated to be applicable to the management of other conditions, such as diabetes control through individualized insulin delivery27–31 and anaesthesia control intra-operatively.32,33 This approach offers a potential avenue for enhancing the precision of antimicrobial therapy across a number of settings where invasive monitoring techniques may not be appropriate, including the community and non-critical care hospital settings. We report the current state of the art within the field of infection that offers a novel approach for the development of closed-loop systems for precision antimicrobial dosing. Concept of closed-loop control for individualized antimicrobial therapy There are several key concepts outlined in Figure 1 that must be considered for the development of closed-loop controllers for antimicrobial therapy. Ideally, monitoring of antimicrobials should be continuous and in a minimally invasive format that does not rely on blood sampling. The development of micro-needle array biosensor technology has provided an opportunity to achieve this, allowing for detection of antimicrobial concentrations in the dermal interstitial fluid (ISF).34,35 This technology has already been validated in the field of diabetes, demonstrating safety and tolerability in human clinical trials and accuracy in diabetic individuals who tend to have poor tissue perfusion due to underlying diabetic vasculopathy.26,34,35 Given that the free antimicrobial concentration in the ISF is generally in equilibrium with the plasma concentration this provides an opportunity for using this technology to monitor ISF concentrations as well as estimate plasma antimicrobial concentration in near real-time without requiring plasma sampling.36–38 This may be challenging in certain situations, such as during periods of tissue hypoperfusion in critically ill patients in the intensive care unit (ICU).39 However, it may also offer a novel option for supporting the optimization of antimicrobial dosing in these populations. This is because the majority of infections occur in tissue ISF.39,40 Therefore, this technology may provide a mechanism for monitoring antimicrobial concentrations in a compartment that is closer to the site where the infection is being treated when compared with plasma.39,40 Figure 1. View largeDownload slide Schematic for closed-loop control of antimicrobial delivery. Figure 1. View largeDownload slide Schematic for closed-loop control of antimicrobial delivery. Data generated by this sensor can then be linked with machine-driven, closed-loop control algorithms such as Proportional-Integral-Derivative (PID)41 and Iterative Learning Controllers (ILC).42 These systems will allow for the optimization of both continuous and bolus (or oral) therapy to drive individualized target attainment of pre-defined PK/PD indices associated with maximal bacterial killing and/or suppression of the emergence of AMR.43,44 These may be current gold standard PK/PD targets45,46 or novel indices, such as AUC:EC50 ratio.47,48 Each of these concepts will individually be explored and critiqued within this manuscript. Microneedles for continuous sensing of agents in the dermal interstitial fluid Microneedle technology was first demonstrated as a suitable mechanism for drug monitoring and delivery over 20 years ago.49 Since then, microneedle technology has progressed rapidly with data supporting the use of microneedles to monitor glucose and lactate concentrations in humans34,35,50,51 as well as acting as delivery systems for drugs and vaccines.30,52 Microneedles work by penetrating the stratum corneum layer of the skin allowing access to the ISF, whilst avoiding the nerve fibres and blood vessels that are found within the dermis. Therefore, this offers a minimally invasive method for drug or metabolite monitoring.34,35,50,51 Side effects such as pain, bleeding, skin reactions, and infection risk have all previously been explored and shown to be minimal following application of such devices to the skin.34 One example of such technology was recently reported by Sharma and colleagues,28 who demonstrated high reproducibility when using microneedle technology to monitor glucose levels in healthy volunteers compared with capillary blood glucose measurements. The authors were able to demonstrate robustness of the device to sterilization using gamma-irradiation thus allowing the device to be sterilized and stored over time for use in monitoring human glucose concentrations.28 Furthermore, this technology can be reproduced reliably and at low cost through the development of scalable microneedle fabrication batch processing, producing up to 300 microneedles every hour.50 However, there are also challenges that remain in the development of microneedles within this field. Whilst microneedle-based methods of microdialysis have also been reported for the monitoring of vancomycin,53 this technique requires transfer of small volumes of ISF, which not only presents technical challenges in maintaining accuracy of the sensor but also leads to delays that mitigate against their application in real-time control.53 Moreover, in clinical trials for monitoring glucose using glucose oxidase-coated microneedles, the sensors appear to occasionally generate artefact during movements that cause them to be partially removed from the intradermal space.28 Whilst the artefact present in previous human studies had a shorter duration than changes in glucose concentration, this still requires consideration. Another challenge encountered with current microneedle sensors in humans has been accuracy of these devices at extreme ranges of glucose, especially hypoglycaemic ranges.28 It is likely therefore that sensor deployment for antimicrobial monitoring will encounter similar barriers for consideration. In addition to microneedle-based sensing, other methods to facilitate continuous monitoring are also under consideration. Probably the most developed are attempts to perform real-time monitoring of drug concentrations in ambulatory animals using invasive vascular catheter insertion.54 These would only be acceptable in very specific situations in clinical practice, such as critical care or at the time of surgery, where tissue hypoperfusion may influence the ability of microneedle devices to accurately predict free drug concentrations in blood. However, invasive devices pose their own risks to the patient, including thrombosis.54 This type of invasive device would not be acceptable in the vast majority of individuals who receive antimicrobial therapy outside of critical care in hospital or in the community settings. A second consideration is the use of non-invasive, sweat-based monitoring systems as have been developed for glucose monitoring. However, to date very few data exist on whether this would be a viable option for monitoring antimicrobial concentrations.55 Antimicrobial electrochemical sensing Electrochemical sensors for antimicrobials in the environment, agriculture, and humans have been demonstrated for a wide range of agents used in human medicine (Table 1). In the literature, electrochemical sensors for the detection and monitoring of antimicrobials are largely based on aptamer, antibody-linked, or enzyme-based sensors.54,56,57 These have demonstrated high sensitivity for monitoring of antimicrobials in potentially physiological ranges seen in ISF.26 However, there remains a paucity of data for many antimicrobial agents to accurately support the ability of these devices to predict the PK in both tissue and plasma at present. Aptamer sensors are nucleic acid-based and are highly specific for their target molecule, producing their signal through the detection of a redox reaction on ligand binding. Engineered using an in vitro selection procedure, called Systematic Evolution of Ligands by EXponential enrichment (SELEX) they have been reported to have a high sensitivity down to the range of picomoles in monitoring of certain environmental contaminants.56 One such aptamer-based sensor is the MEDIC device, described by Ferguson and colleagues.54 This device has been demonstrated in live animal models to be able to monitor in real time a number of different agents, including kanamycin, using a liquid phase filter to prevent interference from blood-fouling of the DNA aptameric sensor.54 Within that study, live rats were injected with increasing doses of kanamycin, an aminoglycoside antibiotic, at hourly intervals to demonstrate the ability to monitor the PK profile in real time using an aptamer sensor in the bloodstream.54 Aminoglycoside aptamers have also been tested against spiked human serum demonstrating accuracy for determining concentrations of routine, clinically observed targets between 2 and 6 μM. Table 1. Current antimicrobial sensor classes reported in the literature Sensor  Setting demonstrated  Ranges of detection in study  Ref  Macrolides  Spiked human urine Water samples Optimal analytical conditions  In spiked human urine: 0–2 μM (azithromycin)  83,84  Quinolones  Spiked human plasma Spiked human urine Milk Optimal analytical conditions  In spiked human plasma: 0.05–100 μM (CIP) 0.1–100 μM (OFX) 0.1–40 μM (NOR) 0.06–100 μM (GAT)  85–90  Chloramphenicol  Milk Spiked human urine Food samples Optimal analytical conditions  In food samples: 0.08–1392 μM LLD 0.015 μM  91–94  Metronidazole  Spiked human urine Optimal analytical conditions  Calibration in lab: Linear range 0.8 pM–720 nM In spiked urine samples reported recovery at concentrations 87, 96, 110, and 123 μM  95  Tetracyclines  Meat/feedstuff samples Spiked honey Optimal analytical conditions  In feedstuff Linear range 0.3–52.0 μM (tetra) LLD 0.10 μM (tetra)  96,97  Rifampicin  Optimal analytical conditions  Linear detection ranges: 0.006–10.0 mmol/L with an LLD of 4.16 nmol/L and 0.04–10 mmol/L with an LLD of 2.34 nmol/L  98  Penicillins  Optimal analytical conditions Food/milk samples  In spiked milk samples: linear range 3–283 μM and LLD 0.3 μM (Pen-G) Recovery from spiked samples was 102±6% In optimal conditions: Km value 67±13 μM reported using Michaelis Menten kinetics equation (Pen-G)  26,58,59,99–113  Aminoglycosides  Optimal analytical conditions Ambulatory animals bloodstream Spiked human serum  In spiked human serum: Accurate within therapeutic range of 2–6 μM  52,98,114–118  Lincomycin  Optimal analytical conditions Foodstuff Spiked human urine  In optimal conditions: Linear detection range up to 1 mM and LLD of 0.08 μM In spiked human urine: Recovery in samples was 96.44% to 103.26%  119  Sulphonamides  Optimal analytical conditions Milk Spiked human urine  In optimal conditions: Range of 0.1–10.0 mmol/L with LLD of 60 nmol/L (TMP) AND 1.0–10.0 mmol/L with LLD of 38 nmol/L (SMX) In spiked urine: Recovery 91.3%-101%  120–122  Sensor  Setting demonstrated  Ranges of detection in study  Ref  Macrolides  Spiked human urine Water samples Optimal analytical conditions  In spiked human urine: 0–2 μM (azithromycin)  83,84  Quinolones  Spiked human plasma Spiked human urine Milk Optimal analytical conditions  In spiked human plasma: 0.05–100 μM (CIP) 0.1–100 μM (OFX) 0.1–40 μM (NOR) 0.06–100 μM (GAT)  85–90  Chloramphenicol  Milk Spiked human urine Food samples Optimal analytical conditions  In food samples: 0.08–1392 μM LLD 0.015 μM  91–94  Metronidazole  Spiked human urine Optimal analytical conditions  Calibration in lab: Linear range 0.8 pM–720 nM In spiked urine samples reported recovery at concentrations 87, 96, 110, and 123 μM  95  Tetracyclines  Meat/feedstuff samples Spiked honey Optimal analytical conditions  In feedstuff Linear range 0.3–52.0 μM (tetra) LLD 0.10 μM (tetra)  96,97  Rifampicin  Optimal analytical conditions  Linear detection ranges: 0.006–10.0 mmol/L with an LLD of 4.16 nmol/L and 0.04–10 mmol/L with an LLD of 2.34 nmol/L  98  Penicillins  Optimal analytical conditions Food/milk samples  In spiked milk samples: linear range 3–283 μM and LLD 0.3 μM (Pen-G) Recovery from spiked samples was 102±6% In optimal conditions: Km value 67±13 μM reported using Michaelis Menten kinetics equation (Pen-G)  26,58,59,99–113  Aminoglycosides  Optimal analytical conditions Ambulatory animals bloodstream Spiked human serum  In spiked human serum: Accurate within therapeutic range of 2–6 μM  52,98,114–118  Lincomycin  Optimal analytical conditions Foodstuff Spiked human urine  In optimal conditions: Linear detection range up to 1 mM and LLD of 0.08 μM In spiked human urine: Recovery in samples was 96.44% to 103.26%  119  Sulphonamides  Optimal analytical conditions Milk Spiked human urine  In optimal conditions: Range of 0.1–10.0 mmol/L with LLD of 60 nmol/L (TMP) AND 1.0–10.0 mmol/L with LLD of 38 nmol/L (SMX) In spiked urine: Recovery 91.3%-101%  120–122  CIP, ciprofloxacin; OFX, ofloxacin; GAT, gatifloxacin; NOR, norfloxacin; TMP, trimethoprim; SMX, sulfamethoxazole; Pen-G, penicillin G; LLD, lower limit of detection. Enzymatic penicillin G sensors are some of the oldest antimicrobial sensors reported in the literature.57 These reactions can be detected through electrical, optical, or calorimetric methods.58 The majority of these techniques detect the hydrolysis of penicillin to penicillinoic acid and a hydrogen ion. One recent example of this technology is reported by Ro-Lee and colleagues utilizing field effect devices.59 The authors describe the high sensitivity of the enzyme-based device, its stability during storage, and re-usability over a 30 day period.59 These mechanisms for antimicrobial sensing have so far been demonstrated on microchips, disc electrodes, and nanotubes. This makes the devices small and highly transportable. This technology must now be transferred and tested on microneedle array devices to explore the sensitivity of such systems for real-time antimicrobial monitoring. However, based on current evidence provided by microdialysis of critically ill patients’ tissue ISF concentrations, this approach is a potential avenue for estimation of antimicrobial concentrations and real-time monitoring.36–38 Preliminary in vitro work exploring the monitoring of β-lactam antibiotics (penicillin G, amoxicillin, and ceftriaxone) in artificial ISF using microneedles has demonstrated such devices provide plausible results.26 However, the major gap in the literature supporting translation currently is a paucity of human, in vivo studies with such biosensors to demonstrate their resistance to biofouling from proteins such as albumin and immunoglobulins.60,61 Furthermore, there remains limited data on the expected free antibiotic concentrations within the ISF for many antibiotics to predict the characteristics of tissue PK and allow accurate estimates of the linear range of response that such sensors will be required to work in before translation into human studies. Closed-loop control for drug delivery Closed-loop controllers have a broad application in the field of diabetes, being the cornerstone of novel developments, such as the artificial pancreas system.31,62 Furthermore, closed-loop control has been demonstrated as effective in controlling delivery of both intravenous and inhaled anaesthetic agents during surgery.32,63 This technology has been demonstrated in pre-clinical and in silico studies to be transferable to optimization of antimicrobial dosing.54,63 Two of the most widely used controllers for continuous and intermittent bolus infusions are the PID and ILC controllers, respectively.43,44 These controllers are algorithms that optimize the delivery of an agent against a pre-determined set point. PID control PID controllers depend on constant monitoring (e.g. every 5 minutes) and can be used to control continuous infusions maintaining drug concentrations at a set target (e.g. either target concentration or PK/PD index). As their name suggests, following data input the PID has three coefficients; the proportional, integral, and derivative. It alters these three coefficients to optimize the response against its target for therapy. The simplicity and robustness of PID algorithms make them extremely suitable for the range of operating conditions found in healthcare. This may be especially useful in critical care, where there is currently a drive towards continuous infusions of β-lactam antimicrobials and nephrotoxic agents, such as vancomycin, to optimize the PK exposure and PD properties.38,64–70 However, where current protocols require sporadic plasma TDM sampling this mechanism offers an opportunity for real-time response to changes in individual patient PK. For example, this would account for variations in PK caused by changes in the patient’s inflammatory response, fluid shifts, augmented renal clearance, and in changing drain outputs in surgical patients that may currently be missed with sporadic TDM sampling.71–74 ILC in closed-loop control ILC provides the option for optimization of bolus or oral therapy, with data from continuous monitoring being used to optimize the amount, timing, and rate at which a bolus (or oral dose) is delivered. Like PID, ILC algorithms have wide applications but work on the assumption that during repetitive tasks (such as antimicrobial bolus dosing at regular intervals) there will be some level of error in target attainment (e.g. overshoot or undershoot). Therefore, the ILC aims to adjust the input, in this case the bolus dose, to reduce the transient error encountered during routine drug delivery to optimize the accuracy of such systems. This may be more applicable to non-critical care or the community setting (such as outpatient parenteral therapy or oral dosing) and in specialist populations, such as paediatrics and pregnancy, where rich data collection will allow for tailored therapy to be determined and adjusted for, based on real-time data and potentially previous experience housed within machine learning algorithms, as has been demonstrated by the use of Case-Based Reasoning in diabetes management.75 These systems can automatically control the delivery of an agent to optimize drug delivery to achieve defined PK/PD targets. If linked with Bayesian dose optimization software or Case-Based Reasoning platforms, which can provide individualized initial dose selection, and novel in vivo mechanisms of predicting antimicrobial PD, these could offer a powerful mechanism for reducing the errors that are commonly observed in the practice of current dose optimization strategies. In terms of translating these into microneedle sensor-driven closed-loop control systems, the biggest challenge remaining is accurately describing the relationship for individual antimicrobials between tissue and plasma PK, especially during the initial phase of dosing, when the drug is not at steady state. This will be required to accurately describe the relationship between free concentrations of drug in both compartments and will likely require rich plasma and microdialysis PK sampling to enable development of accurate algorithms to support such controllers. Additional PK/PD indices for individualizing therapy Currently, individualized PK/PD indices rely on factors such as the MIC to form part of time- and concentration-dependent measures for exposure response (such as AUC:MIC, Time>MIC, or Peak:MIC). MIC as a PD target requires isolation of the causative pathogen and determination of the individual organism’s susceptibility. This causes a practical problem in cases where the invading pathogen is not identified, as is observed during the empirical phase of antimicrobial therapy, and in a significant proportion of cases of sepsis that remain culture-negative throughout the treatment period.76,77 Therefore, in the absence of microbiology results, population-level assumptions are made about the most likely organism causing the infection and the average MIC of this population. Thus this does not provide a truly individualized index on which to optimize antimicrobial therapy. Furthermore, in place of an easily available individualized PK/PD index to guide the assessment of response to therapy, clinicians rely on clinical judgement, physiological parameters, and biochemical markers such as C-reactive protein (CRP) and procalcitonin (PCT) to assess individual patient response.78,79 In particular, CRP, an acute phase protein that is a non-specific marker of inflammation, is one of the most commonly used biomarkers during infection management in clinical practice.80–82 Despite its wide use in infection management, very little attempt has been made to link it directly to exposure–response using PK/PD modelling. To address this, recent studies have reported the use of the ratio of the AUC to the EC50 in paediatric populations.47,48 The EC50 is the concentration of a drug (mg/L) that is estimated to induce a half-maximal antibacterial effect (such as reduction in serum CRP or galactomannan, a specific plasma marker in Aspergillus infection) for an individual patient. The AUC:EC50 ratio can provide an in vivo estimate of drug response by linking drug exposure with PD.47,48 Acting as an in vivo measure of potency, AUC:EC50 enables an estimate of the host immune response to the invading organism. This has the potential to circumvent the problems associated with in vitro MIC estimation and may provide data that can drive the development of real-time algorithms for the delivery and control of individualized antimicrobial therapy. With the clinical validation of tools such as the AUC:EC50 for predicting antimicrobial PD in individuals using markers such as CRP, future work must now explore the role of using newer infection-related biomarkers, such as procalcitonin and CD64 for improving the accuracy of these tools. Furthermore, exploration of similar methods for predicting toxicity (e.g. renal toxicity) may further enhance the individualization of therapy by including host, antimicrobial agent, and pathogen factors in estimations of the outcome of therapy. Drug delivery Whilst intravenous and oral delivery of agents, via infusion pump and personalized dosing alerts respectively, may be the initial routes for antimicrobial delivery using such control systems there is also the potential for delivery via microneedle systems in the future. Such microneedles are now under investigation for drug and vaccine delivery that provide dual functions of sensing and also drug delivery.52 However, in the field of infection, the rate of drug delivery that can be achieved may be hindered by certain drug characteristics (such as hydrophilic versus hydrophobic agents) and the volume of agent required to be delivered. However, this technology may pose an interesting avenue for certain challenging cohorts, such as paediatric patients, as well as for local antimicrobial therapy delivery, such as skin and soft tissue infections or penetration of collections. Conclusions Novel systems are urgently required to individualize delivery of antimicrobial therapy, to address the wide variations in PK currently observed across a range of patient populations, and minimize the impact of sub-optimal dosing on clinical outcomes and AMR. Closed-loop control utilizing dermal antimicrobial sensing techniques offers a potential new avenue of applied research that addresses many of the current barriers associated with drug monitoring and dose optimization tools. Furthermore, the nature of minimally invasive sensor technology provides a platform that can be used across a range of settings from the community to those in intensive care. To achieve this there must be cross-disciplinary collaboration to explore the utility of such technologies to optimize the precision of antimicrobial therapy by addressing a number of the hurdles that remain to implementing this type of technology. Acknowledgements We thank the National Institute of Health Research Imperial Biomedical Research Centre and the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London in partnership with Public Health England and the NIHR Imperial Patient Safety Translational Research Centre. Funding This report is independent research funded by the National Institute for Health Research Invention for Innovation Grant (i4i), Enhanced, Personalized and Integrated Care for Infection Management at Point of Care (EPIC IMPOC), II-LA-0214-20008. A. C. G. is an NIHR Research Professor. This report was also supported by grants from (i) the Engineering, Medicine, Natural Sciences and Physical Sciences Bridging Research in Antimicrobial resistance: Collaboration and Exchange (EMBRACE), Imperial College Antimicrobial Research Collaborative; and (ii) Imperial College Biomedical Research Centre (BRC). J. A. R. wishes to recognize funding from the Australian National Health and Medical Research Council for Centre of Research Excellence (APP1099452) and a Practitioner Fellowship (APP1117065). Transparency declarations None to declare. Author contributions All authors contributed significantly to the literature review and writing of the manuscript. T. M. R. wrote the initial draft of the manuscript with all authors significantly contributing to the development and finalization of the version for submission. Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the UK Department of Health. References 1 O’Neill J. 2016. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations. https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf. 2 Holmes AH, Moore LSP, Sundsfjord A et al.   Understanding the mechanisms and drivers of antimicrobial resistance. Lancet  2016; 387: 176– 87. Google Scholar CrossRef Search ADS   3 Roberts JA, Abdul-Aziz MH, Lipman J et al.   Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions. Lancet Infect Dis  2014; 14: 498– 509. Google Scholar CrossRef Search ADS   4 Perez F, El Chakhtoura NG, Papp-Wallace K et al.   Treatment options for infections caused by carbapenem-resistant Enterobacteriaceae: can we apply ‘precision medicine’ to antimicrobial chemotherapy? Expert Opin Pharmacother  2016; 17: 761– 81. Google Scholar CrossRef Search ADS   5 Abdul-Aziz MH, Roberts JA, Lipman J et al.   Applying pharmacokinetic/pharmacodynamic principles in critically ill patients: optimizing efficacy and reducing resistance development. Semin Respir Crit Care Med  2015; 36: 136– 53. Google Scholar CrossRef Search ADS   6 Cotta MO, Roberts JA, Lipman J. We need to optimize piperacillin-tazobactam dosing in critically ill patients—but how? Crit Care  2016; 20: 163. Google Scholar CrossRef Search ADS   7 Brusselaers N, Vogelaers D, Blot S. The rising problem of antimicrobial resistance in the intensive care unit. Ann Intensive Care  2011; 1: 47. Google Scholar CrossRef Search ADS   8 Charmillon A, Novy E, Agrinier N et al.   The ANTIBIOPERF study: a nationwide cross-sectional survey about practices for β-lactam administration and therapeutic drug monitoring among critically ill patients in France. Clin Microbiol Infect  2016; 22: 625– 31. Google Scholar CrossRef Search ADS   9 Gonçalves-Pereira J, Póvoa P. Antibiotics in critically ill patients: a systematic review of the pharmacokinetics of β-lactams. Crit Care  2011; 15: R206. Google Scholar CrossRef Search ADS   10 Huttner A, Harbarth S, Hope WW et al.   Therapeutic drug monitoring of the β-lactam antibiotics: what is the evidence and which patients should we be using it for? J Antimicrob Chemother  2015; 70: 3178– 83. 11 Patel BM, Paratz J, See NC et al.   Therapeutic drug monitoring of β-lactam antibiotics in burns patients–a one-year prospective study. Ther Drug Monit  2012; 34: 160– 4. Google Scholar CrossRef Search ADS   12 Roberts JA, Norris R, Paterson DL et al.   Therapeutic drug monitoring of antimicrobials. Br J Clin Pharmacol  2012; 73: 27– 36. Google Scholar CrossRef Search ADS   13 Roberts JA, Paul SK, Akova M et al.   DALI: Defining antibiotic levels in intensive care unit patients: Are current β-lactam antibiotic doses sufficient for critically ill patients? Clin Infect Dis  2014; 58: 1072– 83. Google Scholar CrossRef Search ADS   14 Felton TW, Roberts JA, Lodise TP et al.   Individualization of piperacillin dosing for critically ill patients: Dosing software to optimize antimicrobial therapy. Antimicrob Agents Chemother  2014; 58: 4094– 102. Google Scholar CrossRef Search ADS   15 Carlier M, Stove V, Wallis SC et al.   Assays for therapeutic drug monitoring of β-lactam antibiotics: a structured review. Int J Antimicrob Agents  2015; 46: 367– 75. Google Scholar CrossRef Search ADS   16 Souza MJ, Kulmann RR, Silva LM et al.   Development and in-house validation of a microbiological assay for determination of cefepime in injectable preparations. J AOAC Int  2006; 89: 1367– 72. 17 Pickering MK, Brown SD. Assays for determination of ertapenem for applications in therapeutic drug monitoring, pharmacokinetics and sample stability. Biomed Chromatogr  2014; 28: 1525– 31. Google Scholar CrossRef Search ADS   18 Miranda Bastos AC, Vandecasteele SJ, Tulkens PM et al.   Development and validation of a high performance liquid chromatography assay for the determination of temocillin in serum of haemodialysis patients. J Pharm Biomed Anal  2014; 90: 192– 7. Google Scholar CrossRef Search ADS   19 Zalewski P, Cielecka-Piontek J, Paczkowska M. Development and validation of stability-indicating HPLC method for simultaneous determination of meropenem and potassium clavulanate. Acta Pol Pharm  2014; 71: 255– 60. 20 Wolff F, Deprez G, Seyler L et al.   Rapid quantification of six β-lactams to optimize dosage regimens in severely septic patients. Talanta  2013; 103: 153– 60. Google Scholar CrossRef Search ADS   21 Rawson TM, Moore LS, Hernandez B et al.   A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clin Microbiol Infect  2017; 23: 524– 32. Google Scholar CrossRef Search ADS   22 Wilhelm AJ, den Burger JCG, Swart EL. Therapeutic drug monitoring by dried blood spot: progress to date and future directions. Clin Pharmacokinet  2014; 53: 961– 73. Google Scholar CrossRef Search ADS   23 Chong H. Paper-based microfluidic point-of-care diagnostic devices for monitoring drug metabolism. J Nanomed Biotherapeut Discov  2013; 3: 1– 2. Google Scholar CrossRef Search ADS   24 Kang JS, Lee MH. Overview of therapeutic drug monitoring. Korean J Intern Med  2009; 24: 1– 10. Google Scholar CrossRef Search ADS   25 Parker SL, Dorofaeff T, Lipman J et al.   Is there a role for microsampling in antibiotic pharmacokinetic studies? Expert Opin Drug Metab Toxicol  2016; 5255: 601– 14. Google Scholar CrossRef Search ADS   26 Rawson TM, Sharma S, Georgiou P et al.   Towards a Minimally Invasive Device for β-lactam Monitoring in Humans. 2017. http://ac.els-cdn.com/S1388248117301856/1-s2.0-S1388248117301856-main.pdf?_tid=8511cda4-7048-11e7-976d-00000aab0f02&acdnat=1500884423_b35db9176145fbd9ccc62d4a0c7c1c04. 27 Reddy M, Herrero P, Sharkawy ME et al.   Metabolic control with the bio-inspired artificial pancreas in adults with type 1 diabetes: a 24-hour randomized controlled crossover study. J Diabetes Sci Technol  2016; 10: 405– 13. Google Scholar CrossRef Search ADS   28 Sharma S, Huang Z, Rogers M et al.   Evaluation of a minimally invasive glucose biosensor for continuous tissue monitoring. Anal Bioanal Chem  2016; 408: 8427– 35. Google Scholar CrossRef Search ADS   29 Yan G, Warner KS, Zhang J et al.   Evaluation needle length and density of microneedle arrays in the pretreatment of skin for transdermal drug delivery. Int J Pharm  2010; 391: 7– 12. Google Scholar CrossRef Search ADS   30 Bariya SH, Gohel MC, Mehta TA et al.   Microneedles: an emerging transdermal drug delivery system. J Pharm Pharmacol  2012; 64: 11– 29. Google Scholar CrossRef Search ADS   31 Trevitt S, Simpson S, Wood A. Artificial pancreas device systems for the closed-loop control of type 1 diabetes: what systems are in development? J Diabetes Sci Technol  2016; 10: 714– 23. Google Scholar CrossRef Search ADS   32 Absalom AR, Sutcliffe N, Kenny GN. Closed-loop control of anesthesia using bispectral index. Anesthesiology  2002; 96: 67– 73. Google Scholar CrossRef Search ADS   33 Madhavan JS, Puri GD, Mathew PJ. Closed-loop isoflurane administration with bispectral index in open heart surgery: randomized controlled trial with manual control. Acta Anaesthesiol Taiwan  2011; 49: 130– 5. Google Scholar CrossRef Search ADS   34 El-Laboudi A, Oliver NS, Cass A et al.   Use of microneedle array devices for continuous glucose monitoring: a review. Diabetes Technol Ther  2013; 15: 101– 15. Google Scholar CrossRef Search ADS   35 Trzebinski J, Sharma S, Radomska-Botelho Moniz A et al.   Microfluidic device to investigate factors affecting performance in biosensors designed for transdermal applications. Lab Chip  2012; 12: 348. Google Scholar CrossRef Search ADS   36 Roberts JA, Udy AA, Jarrett P et al.   Plasma and target-site subcutaneous tissue population pharmacokinetics and dosing simulations of cefazolin in post-trauma critically ill patients. J Antimicrob Chemother  2014; 70: 1495– 502. Google Scholar CrossRef Search ADS   37 Johnson BL, Damer KM, Walroth TA et al.   A systematic review of vancomycin dosing and monitoring in burn patients. J Burn Care Res  2015; 36: 641– 50. Google Scholar CrossRef Search ADS   38 Roberts JA, Roberts MS, Robertson TA et al.   Piperacillin penetration into tissue of critically ill patients with sepsis—bolus versus continuous administration? Crit Care Med  2009; 37: 926– 33. Google Scholar CrossRef Search ADS   39 Vincent J-L, Bassetti M, François B et al.   Advances in antibiotic therapy in the critically ill. Crit Care  2016; 20: 133. Google Scholar CrossRef Search ADS   40 Joukhadar C, Frossard M, Mayer BX et al.   Impaired target site penetration of β-lactams may account for therapeutic failure in patients with septic shock. Crit Care Med  2001; 29: 385– 91. Google Scholar CrossRef Search ADS   41 Johnson MA, Moradi MH, eds. PID Control . London: Springer, 2005. http://link.springer.com/10.1007/1-84628-148-2. Google Scholar CrossRef Search ADS   42 Ahn H-S, Chen Y, Moore KL. Iterative Learning Control . London: Springer London, 2007. http://link.springer.com/10.1007/978-1-84628-859-3. Google Scholar CrossRef Search ADS   43 Wang Y, Gao F, Doyle FJ. Survey on iterative learning control, repetitive control, and run-to-run control. J Process Control  2009; 19: 1589– 600. Google Scholar CrossRef Search ADS   44 Madady A. PID type iterative learning control with optimal gains. Int J Control Autom Syst  2008; 6: 194– 203. 45 Nielsen EI, Friberg LE. Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Pharmacol Rev  2013; 65: 1053– 90. Google Scholar CrossRef Search ADS   46 Frimodt-Møller N. How predictive is PK/PD for antibacterial agents? Int J Antimicrob Agents  2002; 19: 333– 9. Google Scholar CrossRef Search ADS   47 Huurneman LJ, Neely M, Veringa A et al.   Pharmacodynamics of voriconazole in children: Further steps along the path to true individualized therapy. Antimicrob Agents Chemother  2016; 60: 2336– 42. Google Scholar CrossRef Search ADS   48 Ramos-Martín V, Neely MN, McGowan P et al.   Population pharmacokinetics and pharmacodynamics of teicoplanin in neonates: making better use of C-reactive protein to deliver individualized therapy. J Antimicrob Chemother  2016; 71: 3168– 78. Google Scholar CrossRef Search ADS   49 Henry S, McAllister DV, Allen MG et al.   Microfabricated microneedles: a novel approach to transdermal drug delivery. J Pharm Sci  1998; 87: 922– 5. Google Scholar CrossRef Search ADS   50 Sharma S, Saeed A, Johnson C et al.   Rapid, low cost prototyping of transdermal devices for personal healthcare monitoring. Sens Biosensing Res  2017; 13: 104– 8. Google Scholar CrossRef Search ADS   51 Moniz ARB, Michelakis K, Trzebinski J et al.   Minimally invasive enzyme microprobes: an alternative approach for continuous glucose monitoring. J Diabetes Sci Technol  2012; 6: 479– 80. Google Scholar CrossRef Search ADS   52 Kim YC, Park JH, Prausnitz MR. Microneedles for drug and vaccine delivery. Adv Drug Deliv Rev  2012; 64: 1547– 68. Google Scholar CrossRef Search ADS   53 Ranamukhaarachchi SA, Padeste C, Dübner M et al.   Integrated hollow microneedle-optofluidic biosensor for therapeutic drug monitoring in sub-nanoliter volumes. Sci Rep  2016; 6: 29075. Google Scholar CrossRef Search ADS   54 Ferguson BS, Hoggarth DA, Maliniak D et al.   Real-time, aptamer-based tracking of circulating therapeutic agents in living animals. Sci Transl Med  2013; 5: 213ra165. Google Scholar CrossRef Search ADS   55 Lee H, Song C, Hong YS et al.   Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module. Sci Adv  2017; 3: e1601314. Google Scholar CrossRef Search ADS   56 Hayat A, Marty JL. Aptamer based electrochemical sensors for emerging environmental pollutants. Front Chem  2014; 2: 41. Google Scholar CrossRef Search ADS   57 Gorchkov DV, Soldatkin AP, Maupas H et al.   Correlation between the electrical charge properties of polymeric membranes and the characteristics of ion field effect transistors or penicillinase based enzymatic field effect transistors. Anal Chim Acta  1996; 331: 217– 23. Google Scholar CrossRef Search ADS   58 Healey BG, Walt DR. Improved fiber-optic chemical sensor for penicillin. Anal Chem  1995; 67: 4471– 6. Google Scholar CrossRef Search ADS   59 Lee SR, Rahman MM, Sawada K et al.   Fabrication of a highly sensitive penicillin sensor based on charge transfer techniques. Biosens Bioelectron  2009; 24: 1877– 82. Google Scholar CrossRef Search ADS   60 O’Hare D. Biosensors and sensor systems. In: Yang G, ed. Body Sensor Networks . Springer, 2014. http://link.springer.com/10.1007/978-1-4471-6374-9. 61 Trouillon R, Combs Z, Patel BA et al.   Comparative study of the effect of various electrode membranes on biofouling and electrochemical measurements. Electrochem Commun  2009; 11: 1409– 13. Google Scholar CrossRef Search ADS   62 Herrero P, Georgiou P, Oliver N et al.   A bio-inspired glucose controller based on pancreatic β-cell physiology. J Diabetes Sci Technol  2012; 6: 606– 16. Google Scholar CrossRef Search ADS   63 Philip A, Rawson TM, Moore LSP et al.   Closed-loop controller systems for the precision delivery of vancomycin: an in silico proof-of-concept. In: Federation of Infection Society Annual Conference, 2016. Abstract 5121. http://www.journalofhospitalinfection.com/article/S0195-6701(16)30516-3/pdf. 64 Ulldemolins M, Vaquer S, Llauradó-Serra M et al.   β-Lactam dosing in critically ill patients with septic shock and continuous renal replacement therapy. Crit Care  2014; 18: 227. Google Scholar CrossRef Search ADS   65 Cataldo MA, Tacconelli E, Grilli E et al.   Continuous versus intermittent infusion of vancomycin for the treatment of Gram-positive infections: systematic review and meta-analysis. J Antimicrob Chemother  2012; 67: 17– 24. Google Scholar CrossRef Search ADS   66 Wysocki M, Delatour F, Faurisson F et al.   Continuous versus intermittent infusion of vancomycin in severe staphylococcal infections: prospective multicenter randomized study. Antimicrob Agents Chemother  2001; 45: 2460– 7. Google Scholar CrossRef Search ADS   67 Ulldemolins M, Martin-Loeches I, Llaurado-Serra M et al.   Piperacillin population pharmacokinetics in critically ill patients with multiple organ dysfunction syndrome receiving continuous venovenous haemodiafiltration: effect of type of dialysis membrane on dosing requirements. J Antimicrob Chemother  2016; 71: 1651– 9. Google Scholar CrossRef Search ADS   68 Roberts JA, Lipman J, Blot S et al.   Better outcomes through continuous infusion of time-dependent antibiotics to critically ill patients? Curr Opin Crit Care  2008; 14: 390– 6. Google Scholar CrossRef Search ADS   69 Roos JF, Lipman J, Kirkpatrick CMJ. Population pharmacokinetics and pharmacodynamics of cefpirome in critically ill patients against Gram-negative bacteria. Intensive Care Med  2007; 33: 781– 8. Google Scholar CrossRef Search ADS   70 Ramos-Martín V, Johnson A, Livermore J et al.   Pharmacodynamics of vancomycin for CoNS infection: experimental basis for optimal use of vancomycin in neonates. J Antimicrob Chemother  2016; 992– 1002. 71 Udy AA, Lipman J, Jarrett P et al.   Are standard doses of piperacillin sufficient for critically ill patients with augmented creatinine clearance? Crit Care  2015; 19: 28. Google Scholar CrossRef Search ADS   72 Chagnac A, Weinstein T, Korzets A et al.   Glomerular hemodynamics in severe obesity. Am J Physiol Renal Physiol  2000; 278: F817– 22. Google Scholar CrossRef Search ADS   73 Shimamoto Y, Fukuda T, Tanaka K et al.   Systemic inflammatory response syndrome criteria and vancomycin dose requirement in patients with sepsis. Intensive Care Med  2013; 39: 1247– 52. Google Scholar CrossRef Search ADS   74 Blot SI, Pea F, Lipman J. The effect of pathophysiology on pharmacokinetics in the critically ill patient - Concepts appraised by the example of antimicrobial agents. Adv Drug Deliv Rev  2014; 77: 3– 11. Google Scholar CrossRef Search ADS   75 Herrero P, Pesl P, Reddy M et al.   Advanced insulin bolus advisor based on run-to-run control and case-based reasoning. IEEE J Biomed Heal Informatics  2015; 19: 1087– 96. 76 Phua J, Ngerng W, See K et al.   Characteristics and outcomes of culture-negative versus culture-positive severe sepsis. Crit Care  2013; 17: R202. Google Scholar CrossRef Search ADS   77 Zarb P, Goossens H. European Surveillance of Antimicrobial Consumption (ESAC): value of a point-prevalence survey of antimicrobial use across Europe. Drugs  2011; 71: 745– 55. Google Scholar CrossRef Search ADS   78 Rawson TM, Charani E, Moore LSP et al.   Mapping the decision pathways of acute infection management in secondary care among UK medical physicians: a qualitative study. BMC Med  2016; 14: 208. Google Scholar CrossRef Search ADS   79 de Jong E, van Oers JA, Beishuizen A et al.   Efficacy and safety of procalcitonin guidance in reducing the duration of antibiotic treatment in critically ill patients: a randomised, controlled, open-label trial. Lancet Infect Dis  2016; 16: 819– 27. Google Scholar CrossRef Search ADS   80 Markanday A. Acute phase reactants in infections: evidence-based review and a guide for clinicians. Open Forum Infect Dis  2015; 2: ofv098. Google Scholar CrossRef Search ADS   81 Kobeissi ZA, Zanotti-Cavazzoni SL. Biomarkers of sepsis. Yearb Crit Care Med  2010; 2010: 227– 8. Google Scholar CrossRef Search ADS   82 Nargis W, Ahamed B, Ibrahim M. Procalcitonin versus C-reactive protein: usefulness as biomarker of sepsis in ICU patient. Int J Crit Illn Inj Sci  2014; 4: 195. Google Scholar CrossRef Search ADS   83 Nagaraj VJ, Jacobs M, Vattipalli KM et al.   Nanochannel-based electrochemical sensor for the detection of pharmaceutical contaminants in water. Environ Sci Process Impacts  2013; 16: 135– 40. Google Scholar CrossRef Search ADS   84 Zhang K, Lu L, Wen Y et al.   Facile synthesis of the necklace-like graphene oxide-multi-walled carbon nanotube nanohybrid and its application in electrochemical sensing of Azithromycin. Anal Chim Acta  2013; 787: 50– 6. Google Scholar CrossRef Search ADS   85 Pinacho DG, Sánchez-Baeza F, Pividori MI et al.   Electrochemical detection of fluoroquinolone antibiotics in milk using a magneto immunosensor. Sensors (Basel)  2014; 14: 15965– 80. Google Scholar CrossRef Search ADS   86 Gayen P, Chaplin BP. Selective electrochemical detection of ciprofloxacin with a porous nafion/multiwalled carbon nanotube composite film electrode. ACS Appl Mater Interfaces  2016; 8: 1615– 26. Google Scholar CrossRef Search ADS   87 Theanponkrang S, Suginta W, Weingart H et al.   Robotic voltammetry with carbon nanotube-based sensors: a superb blend for convenient high-quality antimicrobial trace analysis. Int J Nanomedicine  2015; 10: 859– 68. 88 Zhang F, Gu S, Ding Y et al.   A novel sensor based on electropolymerization of β-cyclodextrin and l-arginine on carbon paste electrode for determination of fluoroquinolones. Anal Chim Acta  2013; 770: 53– 61. Google Scholar CrossRef Search ADS   89 Giroud F, Gorgy K, Gondran C et al.   Impedimetric immunosensor based on a polypyrrole-antibiotic model film for the label-free picomolar detection of ciprofloxacin. Anal Chem  2009; 81: 8405– 9. Google Scholar CrossRef Search ADS   90 Moraes FC, Silva TA, Cesarino I et al.   Antibiotic detection in urine using electrochemical sensors based on vertically aligned carbon nanotubes. Electroanalysis  2013; 25: 2092– 9. Google Scholar CrossRef Search ADS   91 Zhang X, Zhang Y-C, Zhang J-W. A highly selective electrochemical sensor for chloramphenicol based on three-dimensional reduced graphene oxide architectures. Talanta  2016; 161: 567– 73. Google Scholar CrossRef Search ADS   92 Jakubec P, Urbanová V, Medříková Z et al.   Advanced sensing of antibiotics with magnetic gold nanocomposite: electrochemical detection of chloramphenicol. Chemistry  2016; 22: 14279– 84. Google Scholar CrossRef Search ADS   93 Govindasamy M, Chen SM, Mani V et al.   Molybdenum disulfide nanosheets coated multiwalled carbon nanotubes composite for highly sensitive determination of chloramphenicol in food samples milk, honey and powdered milk. J Colloid Interface Sci  2017; 485: 129– 36. Google Scholar CrossRef Search ADS   94 Karthik R, Govindasamy M, Chen SM et al.   Green synthesized gold nanoparticles decorated graphene oxide for sensitive determination of chloramphenicol in milk, powdered milk, honey and eye drops. J Colloid Interface Sci  2016; 475: 46– 56. Google Scholar CrossRef Search ADS   95 Meenakshi S, Pandian K, Jayakumari LS et al.   Enhanced amperometric detection of metronidazole in drug formulations and urine samples based on chitosan protected tetrasulfonated copper phthalocyanine thin-film modified glassy carbon electrode. Mater Sci Eng C Mater Biol Appl  2016; 59: 136– 44. Google Scholar CrossRef Search ADS   96 Gan T, Shi Z, Sun J, Liu Y. Simple and novel electrochemical sensor for the determination of tetracycline based on iron/zinc cations-exchanged montmorillonite catalyst. Talanta  2014; 121: 187– 93. Google Scholar CrossRef Search ADS   97 Liu S, Wang Y, Xu W et al.   A novel sandwich-type electrochemical aptasensor based on GR-3D Au and aptamer-AuNPs-HRP for sensitive detection of oxytetracycline. Biosens Bioelectron  2017; 88: 181– 7. Google Scholar CrossRef Search ADS   98 Rastgar S, Shahrokhian S. Nickel hydroxide nanoparticles-reduced graphene oxide nanosheets film: Layer-by-layer electrochemical preparation, characterization and rifampicin sensory application. Talanta  2014; 119: 156– 63. Google Scholar CrossRef Search ADS   99 Janata J, City SL. pH-based enzyme potentiometric sensors. Anal Chem  1985; 57: 1924– 5. Google Scholar CrossRef Search ADS   100 Anzai J, Hashimoto J, Osa T et al.   Penicillin sensors based on an ion-sensitive coated with stearic acid Langmuir-Blodgett field effect membrane transistor. Anal Sci  1988; 4: 247– 50. Google Scholar CrossRef Search ADS   101 Yerian TD, Christian GD, Ruzicka J. Flow injection analysis as a diagnostic technique for development and testing of chemical sensors. Anal Chim Acta  1988; 204: 7– 28. Google Scholar CrossRef Search ADS   102 Gao X, Zhen R, Zhang Y, Grimes CA. Detecting penicillin in milk with a wireless magnetoelastic biosensor. Sen Lett  2009; 7: 6– 10. Google Scholar CrossRef Search ADS   103 Wang H, Wang Y, Liu S et al.   Signal-on electrochemical detection of antibiotics at zeptomole level based on target-aptamer binding triggered multiple recycling amplification. Biosens Bioelectron  2016; 80: 471– 6. Google Scholar CrossRef Search ADS   104 Daprà J, Lauridsen LH, Nielsen AT et al.   Comparative study on aptamers as recognition elements for antibiotics in a label-free all-polymer biosensor. Biosens Bioelectron  2013; 43: 315– 20. Google Scholar CrossRef Search ADS   105 Pikkemaat MG. Microbial screening methods for detection of antibiotic residues in slaughter animals. Anal Bioanal Chem  2009; 395: 893– 905. Google Scholar CrossRef Search ADS   106 Huet AC, Delahaut P, Fodey T et al.   Advances in biosensor-based analysis for antimicrobial residues in foods. Trends Anal Chem  2010; 29: 1281– 94. Google Scholar CrossRef Search ADS   107 Willander M, Khun K, Ibupoto ZH. Metal oxide nanosensors using polymeric membranes, enzymes and antibody receptors as ion and molecular recognition elements. Sensors (Switzerland)  2014; 14: 8605– 32. Google Scholar CrossRef Search ADS   108 Ismail F, Adeloju SB. Galvanostatic entrapment of penicillinase into polytyramine films and its utilization for the potentiometric determination of penicillin. Sensors  2010; 10: 2851– 68. Google Scholar CrossRef Search ADS   109 Bi X, Hartono D, Yang KL. Real-time liquid crystal pH sensor for monitoring enzymatic activities of penicillinase. Adv Funct Mater  2009; 19: 3760– 5. Google Scholar CrossRef Search ADS   110 Gonçalves LM, Callera WFA, Sotomayor MDPT et al.   Penicillinase-based amperometric biosensor for penicillin G. Electrochem Commun  2014; 38: 131– 3. Google Scholar CrossRef Search ADS   111 Müntze GM, Baur B, Schäfer W et al.   Quantitative analysis of immobilized penicillinase using enzyme-modified AlGaN/GaN field-effect transistors. Biosens Bioelectron  2015; 64: 605– 10. Google Scholar CrossRef Search ADS   112 Siqueira JR, Abouzar MH, Poghossian A et al.   Penicillin biosensor based on a capacitive field-effect structure functionalized with a dendrimer/carbon nanotube multilayer. Biosens Bioelectron  2009; 25: 497– 501. Google Scholar CrossRef Search ADS   113 Cháfer-Pericás C, Maquieira Á, Puchades R. Fast screening methods to detect antibiotic residues in food samples. Trends Anal Chem  2010; 29: 1038– 49. Google Scholar CrossRef Search ADS   114 Rowe AA, Miller EA, Plaxco KW. Reagentless measurement of aminoglycoside antibiotics in blood serum via an electrochemical, ribonucleic acid aptamer-based biosensor. Anal Chem  2010; 82: 7090– 5. Google Scholar CrossRef Search ADS   115 Wu X, Kuang H, Hao C et al.   Paper supported immunosensor for detection of antibiotics. Biosens Bioelectron  2012; 33: 309– 12. Google Scholar CrossRef Search ADS   116 Schoukroun-Barnes LR, Wagan S, White RJ. Enhancing the analytical performance of electrochemical RNA aptamer-based sensors for sensitive detection of aminoglycoside antibiotics. Anal Chem  2014; 86: 1131– 7. Google Scholar CrossRef Search ADS   117 Han S, Li B, Song Z et al.   A kanamycin sensor based on an electrosynthesized molecularly imprinted poly-o-phenylenediamine film on a single-walled carbon nanohorn modified glassy carbon electrode. Analyst  2016; 142: 218– 23. Google Scholar CrossRef Search ADS   118 Nikolaus N, Strehlitz B. DNA-aptamers binding aminoglycoside antibiotics. Sensors (Basel)  2014; 14: 3737– 55. Google Scholar CrossRef Search ADS   119 Chiu M-H, Yang H-H, Liu C-H et al.   Determination of lincomycin in urine and some foodstuffs by flow injection analysis coupled with liquid chromatography and electrochemical detection with a preanodized screen-printed carbon electrode. J Chromatogr B Analyt Technol Biomed Life Sci  2009; 877: 991– 4. Google Scholar CrossRef Search ADS   120 Zacco E, Adrian J, Galve R et al.   Electrochemical magneto immunosensing of antibiotic residues in milk. Biosens Bioelectron  2007; 22: 2184– 91. Google Scholar CrossRef Search ADS   121 Joseph R, Girish Kumar K. Differential pulse voltammetric determination and catalytic oxidation of sulfamethoxazole using [5,10,15,20- tetrakis (3-methoxy-4-hydroxy phenyl) porphyrinato] Cu (II) modified carbon paste sensor. Drug Test Anal  2010; 2: 278– 83. Google Scholar CrossRef Search ADS   122 Sgobbi LF, Razzino CA, Machado SAS. A disposable electrochemical sensor for simultaneous detection of sulfamethoxazole and trimethoprim antibiotics in urine based on multiwalled nanotubes decorated with Prussian blue nanocubes modified screen-printed electrode. Electrochim Acta  2016; 191: 1010– 7. Google Scholar CrossRef Search ADS   © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Antimicrobial Chemotherapy Oxford University Press

Delivering precision antimicrobial therapy through closed-loop control systems

Loading next page...
 
/lp/ou_press/delivering-precision-antimicrobial-therapy-through-closed-loop-control-ii83kYSamr
Publisher
Oxford University Press
Copyright
© The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.
ISSN
0305-7453
eISSN
1460-2091
D.O.I.
10.1093/jac/dkx458
Publisher site
See Article on Publisher Site

Abstract

Abstract Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients. Introduction Antimicrobial resistance (AMR) threatens to be a leading cause of death by 20501 making it a global patient safety issue. A major driver of AMR is the inappropriate use of antimicrobials in humans and animals.2 To date, research in this field has focused on optimizing the selection of antimicrobial agents. However, these strategies often fail to also consider optimization of the dose of the antimicrobial agent, which should aim to be sufficient to maximize bacterial killing whilst negating the harmful consequences of therapy, such as development of AMR and toxicity to the host. Data are emerging within certain patient populations, such as critically ill patients, describing wide variations in how individuals handle antimicrobials (pharmacokinetics; PK).3–7 These wide variations in individual PK appear to be associated with increased variation in the effects of therapy, including outcomes of treatment and the development of AMR (their pharmacodynamics; PD).3–7 In response to the observed variations in individual PK, there has been a shift in the focus of therapeutic drug monitoring (TDM) away from primarily being used to prevent toxicity caused by antimicrobials with narrow therapeutic windows, towards enhancing the efficacy of less toxic agents such as the β-lactams, in order to optimize the outcomes of treatment.4,8–13 However, to achieve true individualization of therapy, we require a focus on not just the PK of antimicrobial agents. We must also understand the individual patient’s physiology as well as the characteristics of the organism that we are treating. One method that has been explored widely is the use of Bayesian dose optimization platforms.3 Whilst TDM linked with Bayesian forecasting provides a powerful opportunity for delivering individualized care for patients,3,14 several gaps in current strategies for dose optimization of antimicrobials have hindered clinical implementation. Most notably, methods for more-continuous monitoring to allow real-time adaptive dosing of agents are still not available. Other challenges include difficulties in access to appropriate antimicrobial assays,12,15–20 poor integration of dosing software with electronic health records and decision support systems,3,21 challenges with collecting and handling PK samples,22,23 and failures of compliance with PK sampling protocols currently being used by healthcare professionals.24 Validation of novel methods for the monitoring and dose optimization of antimicrobial agents is required. Whilst several studies have explored the role of microfluidics,22,23,25 these are still hindered by many of the problems associated with routine antimicrobial TDM strategies, such as the need for laboratory analysis and transport of blood products. One potential method for avoiding these problems is the development of closed-loop systems based on minimally invasive, microneedle electrochemical sensor technology.26 This technology has been demonstrated to be applicable to the management of other conditions, such as diabetes control through individualized insulin delivery27–31 and anaesthesia control intra-operatively.32,33 This approach offers a potential avenue for enhancing the precision of antimicrobial therapy across a number of settings where invasive monitoring techniques may not be appropriate, including the community and non-critical care hospital settings. We report the current state of the art within the field of infection that offers a novel approach for the development of closed-loop systems for precision antimicrobial dosing. Concept of closed-loop control for individualized antimicrobial therapy There are several key concepts outlined in Figure 1 that must be considered for the development of closed-loop controllers for antimicrobial therapy. Ideally, monitoring of antimicrobials should be continuous and in a minimally invasive format that does not rely on blood sampling. The development of micro-needle array biosensor technology has provided an opportunity to achieve this, allowing for detection of antimicrobial concentrations in the dermal interstitial fluid (ISF).34,35 This technology has already been validated in the field of diabetes, demonstrating safety and tolerability in human clinical trials and accuracy in diabetic individuals who tend to have poor tissue perfusion due to underlying diabetic vasculopathy.26,34,35 Given that the free antimicrobial concentration in the ISF is generally in equilibrium with the plasma concentration this provides an opportunity for using this technology to monitor ISF concentrations as well as estimate plasma antimicrobial concentration in near real-time without requiring plasma sampling.36–38 This may be challenging in certain situations, such as during periods of tissue hypoperfusion in critically ill patients in the intensive care unit (ICU).39 However, it may also offer a novel option for supporting the optimization of antimicrobial dosing in these populations. This is because the majority of infections occur in tissue ISF.39,40 Therefore, this technology may provide a mechanism for monitoring antimicrobial concentrations in a compartment that is closer to the site where the infection is being treated when compared with plasma.39,40 Figure 1. View largeDownload slide Schematic for closed-loop control of antimicrobial delivery. Figure 1. View largeDownload slide Schematic for closed-loop control of antimicrobial delivery. Data generated by this sensor can then be linked with machine-driven, closed-loop control algorithms such as Proportional-Integral-Derivative (PID)41 and Iterative Learning Controllers (ILC).42 These systems will allow for the optimization of both continuous and bolus (or oral) therapy to drive individualized target attainment of pre-defined PK/PD indices associated with maximal bacterial killing and/or suppression of the emergence of AMR.43,44 These may be current gold standard PK/PD targets45,46 or novel indices, such as AUC:EC50 ratio.47,48 Each of these concepts will individually be explored and critiqued within this manuscript. Microneedles for continuous sensing of agents in the dermal interstitial fluid Microneedle technology was first demonstrated as a suitable mechanism for drug monitoring and delivery over 20 years ago.49 Since then, microneedle technology has progressed rapidly with data supporting the use of microneedles to monitor glucose and lactate concentrations in humans34,35,50,51 as well as acting as delivery systems for drugs and vaccines.30,52 Microneedles work by penetrating the stratum corneum layer of the skin allowing access to the ISF, whilst avoiding the nerve fibres and blood vessels that are found within the dermis. Therefore, this offers a minimally invasive method for drug or metabolite monitoring.34,35,50,51 Side effects such as pain, bleeding, skin reactions, and infection risk have all previously been explored and shown to be minimal following application of such devices to the skin.34 One example of such technology was recently reported by Sharma and colleagues,28 who demonstrated high reproducibility when using microneedle technology to monitor glucose levels in healthy volunteers compared with capillary blood glucose measurements. The authors were able to demonstrate robustness of the device to sterilization using gamma-irradiation thus allowing the device to be sterilized and stored over time for use in monitoring human glucose concentrations.28 Furthermore, this technology can be reproduced reliably and at low cost through the development of scalable microneedle fabrication batch processing, producing up to 300 microneedles every hour.50 However, there are also challenges that remain in the development of microneedles within this field. Whilst microneedle-based methods of microdialysis have also been reported for the monitoring of vancomycin,53 this technique requires transfer of small volumes of ISF, which not only presents technical challenges in maintaining accuracy of the sensor but also leads to delays that mitigate against their application in real-time control.53 Moreover, in clinical trials for monitoring glucose using glucose oxidase-coated microneedles, the sensors appear to occasionally generate artefact during movements that cause them to be partially removed from the intradermal space.28 Whilst the artefact present in previous human studies had a shorter duration than changes in glucose concentration, this still requires consideration. Another challenge encountered with current microneedle sensors in humans has been accuracy of these devices at extreme ranges of glucose, especially hypoglycaemic ranges.28 It is likely therefore that sensor deployment for antimicrobial monitoring will encounter similar barriers for consideration. In addition to microneedle-based sensing, other methods to facilitate continuous monitoring are also under consideration. Probably the most developed are attempts to perform real-time monitoring of drug concentrations in ambulatory animals using invasive vascular catheter insertion.54 These would only be acceptable in very specific situations in clinical practice, such as critical care or at the time of surgery, where tissue hypoperfusion may influence the ability of microneedle devices to accurately predict free drug concentrations in blood. However, invasive devices pose their own risks to the patient, including thrombosis.54 This type of invasive device would not be acceptable in the vast majority of individuals who receive antimicrobial therapy outside of critical care in hospital or in the community settings. A second consideration is the use of non-invasive, sweat-based monitoring systems as have been developed for glucose monitoring. However, to date very few data exist on whether this would be a viable option for monitoring antimicrobial concentrations.55 Antimicrobial electrochemical sensing Electrochemical sensors for antimicrobials in the environment, agriculture, and humans have been demonstrated for a wide range of agents used in human medicine (Table 1). In the literature, electrochemical sensors for the detection and monitoring of antimicrobials are largely based on aptamer, antibody-linked, or enzyme-based sensors.54,56,57 These have demonstrated high sensitivity for monitoring of antimicrobials in potentially physiological ranges seen in ISF.26 However, there remains a paucity of data for many antimicrobial agents to accurately support the ability of these devices to predict the PK in both tissue and plasma at present. Aptamer sensors are nucleic acid-based and are highly specific for their target molecule, producing their signal through the detection of a redox reaction on ligand binding. Engineered using an in vitro selection procedure, called Systematic Evolution of Ligands by EXponential enrichment (SELEX) they have been reported to have a high sensitivity down to the range of picomoles in monitoring of certain environmental contaminants.56 One such aptamer-based sensor is the MEDIC device, described by Ferguson and colleagues.54 This device has been demonstrated in live animal models to be able to monitor in real time a number of different agents, including kanamycin, using a liquid phase filter to prevent interference from blood-fouling of the DNA aptameric sensor.54 Within that study, live rats were injected with increasing doses of kanamycin, an aminoglycoside antibiotic, at hourly intervals to demonstrate the ability to monitor the PK profile in real time using an aptamer sensor in the bloodstream.54 Aminoglycoside aptamers have also been tested against spiked human serum demonstrating accuracy for determining concentrations of routine, clinically observed targets between 2 and 6 μM. Table 1. Current antimicrobial sensor classes reported in the literature Sensor  Setting demonstrated  Ranges of detection in study  Ref  Macrolides  Spiked human urine Water samples Optimal analytical conditions  In spiked human urine: 0–2 μM (azithromycin)  83,84  Quinolones  Spiked human plasma Spiked human urine Milk Optimal analytical conditions  In spiked human plasma: 0.05–100 μM (CIP) 0.1–100 μM (OFX) 0.1–40 μM (NOR) 0.06–100 μM (GAT)  85–90  Chloramphenicol  Milk Spiked human urine Food samples Optimal analytical conditions  In food samples: 0.08–1392 μM LLD 0.015 μM  91–94  Metronidazole  Spiked human urine Optimal analytical conditions  Calibration in lab: Linear range 0.8 pM–720 nM In spiked urine samples reported recovery at concentrations 87, 96, 110, and 123 μM  95  Tetracyclines  Meat/feedstuff samples Spiked honey Optimal analytical conditions  In feedstuff Linear range 0.3–52.0 μM (tetra) LLD 0.10 μM (tetra)  96,97  Rifampicin  Optimal analytical conditions  Linear detection ranges: 0.006–10.0 mmol/L with an LLD of 4.16 nmol/L and 0.04–10 mmol/L with an LLD of 2.34 nmol/L  98  Penicillins  Optimal analytical conditions Food/milk samples  In spiked milk samples: linear range 3–283 μM and LLD 0.3 μM (Pen-G) Recovery from spiked samples was 102±6% In optimal conditions: Km value 67±13 μM reported using Michaelis Menten kinetics equation (Pen-G)  26,58,59,99–113  Aminoglycosides  Optimal analytical conditions Ambulatory animals bloodstream Spiked human serum  In spiked human serum: Accurate within therapeutic range of 2–6 μM  52,98,114–118  Lincomycin  Optimal analytical conditions Foodstuff Spiked human urine  In optimal conditions: Linear detection range up to 1 mM and LLD of 0.08 μM In spiked human urine: Recovery in samples was 96.44% to 103.26%  119  Sulphonamides  Optimal analytical conditions Milk Spiked human urine  In optimal conditions: Range of 0.1–10.0 mmol/L with LLD of 60 nmol/L (TMP) AND 1.0–10.0 mmol/L with LLD of 38 nmol/L (SMX) In spiked urine: Recovery 91.3%-101%  120–122  Sensor  Setting demonstrated  Ranges of detection in study  Ref  Macrolides  Spiked human urine Water samples Optimal analytical conditions  In spiked human urine: 0–2 μM (azithromycin)  83,84  Quinolones  Spiked human plasma Spiked human urine Milk Optimal analytical conditions  In spiked human plasma: 0.05–100 μM (CIP) 0.1–100 μM (OFX) 0.1–40 μM (NOR) 0.06–100 μM (GAT)  85–90  Chloramphenicol  Milk Spiked human urine Food samples Optimal analytical conditions  In food samples: 0.08–1392 μM LLD 0.015 μM  91–94  Metronidazole  Spiked human urine Optimal analytical conditions  Calibration in lab: Linear range 0.8 pM–720 nM In spiked urine samples reported recovery at concentrations 87, 96, 110, and 123 μM  95  Tetracyclines  Meat/feedstuff samples Spiked honey Optimal analytical conditions  In feedstuff Linear range 0.3–52.0 μM (tetra) LLD 0.10 μM (tetra)  96,97  Rifampicin  Optimal analytical conditions  Linear detection ranges: 0.006–10.0 mmol/L with an LLD of 4.16 nmol/L and 0.04–10 mmol/L with an LLD of 2.34 nmol/L  98  Penicillins  Optimal analytical conditions Food/milk samples  In spiked milk samples: linear range 3–283 μM and LLD 0.3 μM (Pen-G) Recovery from spiked samples was 102±6% In optimal conditions: Km value 67±13 μM reported using Michaelis Menten kinetics equation (Pen-G)  26,58,59,99–113  Aminoglycosides  Optimal analytical conditions Ambulatory animals bloodstream Spiked human serum  In spiked human serum: Accurate within therapeutic range of 2–6 μM  52,98,114–118  Lincomycin  Optimal analytical conditions Foodstuff Spiked human urine  In optimal conditions: Linear detection range up to 1 mM and LLD of 0.08 μM In spiked human urine: Recovery in samples was 96.44% to 103.26%  119  Sulphonamides  Optimal analytical conditions Milk Spiked human urine  In optimal conditions: Range of 0.1–10.0 mmol/L with LLD of 60 nmol/L (TMP) AND 1.0–10.0 mmol/L with LLD of 38 nmol/L (SMX) In spiked urine: Recovery 91.3%-101%  120–122  CIP, ciprofloxacin; OFX, ofloxacin; GAT, gatifloxacin; NOR, norfloxacin; TMP, trimethoprim; SMX, sulfamethoxazole; Pen-G, penicillin G; LLD, lower limit of detection. Enzymatic penicillin G sensors are some of the oldest antimicrobial sensors reported in the literature.57 These reactions can be detected through electrical, optical, or calorimetric methods.58 The majority of these techniques detect the hydrolysis of penicillin to penicillinoic acid and a hydrogen ion. One recent example of this technology is reported by Ro-Lee and colleagues utilizing field effect devices.59 The authors describe the high sensitivity of the enzyme-based device, its stability during storage, and re-usability over a 30 day period.59 These mechanisms for antimicrobial sensing have so far been demonstrated on microchips, disc electrodes, and nanotubes. This makes the devices small and highly transportable. This technology must now be transferred and tested on microneedle array devices to explore the sensitivity of such systems for real-time antimicrobial monitoring. However, based on current evidence provided by microdialysis of critically ill patients’ tissue ISF concentrations, this approach is a potential avenue for estimation of antimicrobial concentrations and real-time monitoring.36–38 Preliminary in vitro work exploring the monitoring of β-lactam antibiotics (penicillin G, amoxicillin, and ceftriaxone) in artificial ISF using microneedles has demonstrated such devices provide plausible results.26 However, the major gap in the literature supporting translation currently is a paucity of human, in vivo studies with such biosensors to demonstrate their resistance to biofouling from proteins such as albumin and immunoglobulins.60,61 Furthermore, there remains limited data on the expected free antibiotic concentrations within the ISF for many antibiotics to predict the characteristics of tissue PK and allow accurate estimates of the linear range of response that such sensors will be required to work in before translation into human studies. Closed-loop control for drug delivery Closed-loop controllers have a broad application in the field of diabetes, being the cornerstone of novel developments, such as the artificial pancreas system.31,62 Furthermore, closed-loop control has been demonstrated as effective in controlling delivery of both intravenous and inhaled anaesthetic agents during surgery.32,63 This technology has been demonstrated in pre-clinical and in silico studies to be transferable to optimization of antimicrobial dosing.54,63 Two of the most widely used controllers for continuous and intermittent bolus infusions are the PID and ILC controllers, respectively.43,44 These controllers are algorithms that optimize the delivery of an agent against a pre-determined set point. PID control PID controllers depend on constant monitoring (e.g. every 5 minutes) and can be used to control continuous infusions maintaining drug concentrations at a set target (e.g. either target concentration or PK/PD index). As their name suggests, following data input the PID has three coefficients; the proportional, integral, and derivative. It alters these three coefficients to optimize the response against its target for therapy. The simplicity and robustness of PID algorithms make them extremely suitable for the range of operating conditions found in healthcare. This may be especially useful in critical care, where there is currently a drive towards continuous infusions of β-lactam antimicrobials and nephrotoxic agents, such as vancomycin, to optimize the PK exposure and PD properties.38,64–70 However, where current protocols require sporadic plasma TDM sampling this mechanism offers an opportunity for real-time response to changes in individual patient PK. For example, this would account for variations in PK caused by changes in the patient’s inflammatory response, fluid shifts, augmented renal clearance, and in changing drain outputs in surgical patients that may currently be missed with sporadic TDM sampling.71–74 ILC in closed-loop control ILC provides the option for optimization of bolus or oral therapy, with data from continuous monitoring being used to optimize the amount, timing, and rate at which a bolus (or oral dose) is delivered. Like PID, ILC algorithms have wide applications but work on the assumption that during repetitive tasks (such as antimicrobial bolus dosing at regular intervals) there will be some level of error in target attainment (e.g. overshoot or undershoot). Therefore, the ILC aims to adjust the input, in this case the bolus dose, to reduce the transient error encountered during routine drug delivery to optimize the accuracy of such systems. This may be more applicable to non-critical care or the community setting (such as outpatient parenteral therapy or oral dosing) and in specialist populations, such as paediatrics and pregnancy, where rich data collection will allow for tailored therapy to be determined and adjusted for, based on real-time data and potentially previous experience housed within machine learning algorithms, as has been demonstrated by the use of Case-Based Reasoning in diabetes management.75 These systems can automatically control the delivery of an agent to optimize drug delivery to achieve defined PK/PD targets. If linked with Bayesian dose optimization software or Case-Based Reasoning platforms, which can provide individualized initial dose selection, and novel in vivo mechanisms of predicting antimicrobial PD, these could offer a powerful mechanism for reducing the errors that are commonly observed in the practice of current dose optimization strategies. In terms of translating these into microneedle sensor-driven closed-loop control systems, the biggest challenge remaining is accurately describing the relationship for individual antimicrobials between tissue and plasma PK, especially during the initial phase of dosing, when the drug is not at steady state. This will be required to accurately describe the relationship between free concentrations of drug in both compartments and will likely require rich plasma and microdialysis PK sampling to enable development of accurate algorithms to support such controllers. Additional PK/PD indices for individualizing therapy Currently, individualized PK/PD indices rely on factors such as the MIC to form part of time- and concentration-dependent measures for exposure response (such as AUC:MIC, Time>MIC, or Peak:MIC). MIC as a PD target requires isolation of the causative pathogen and determination of the individual organism’s susceptibility. This causes a practical problem in cases where the invading pathogen is not identified, as is observed during the empirical phase of antimicrobial therapy, and in a significant proportion of cases of sepsis that remain culture-negative throughout the treatment period.76,77 Therefore, in the absence of microbiology results, population-level assumptions are made about the most likely organism causing the infection and the average MIC of this population. Thus this does not provide a truly individualized index on which to optimize antimicrobial therapy. Furthermore, in place of an easily available individualized PK/PD index to guide the assessment of response to therapy, clinicians rely on clinical judgement, physiological parameters, and biochemical markers such as C-reactive protein (CRP) and procalcitonin (PCT) to assess individual patient response.78,79 In particular, CRP, an acute phase protein that is a non-specific marker of inflammation, is one of the most commonly used biomarkers during infection management in clinical practice.80–82 Despite its wide use in infection management, very little attempt has been made to link it directly to exposure–response using PK/PD modelling. To address this, recent studies have reported the use of the ratio of the AUC to the EC50 in paediatric populations.47,48 The EC50 is the concentration of a drug (mg/L) that is estimated to induce a half-maximal antibacterial effect (such as reduction in serum CRP or galactomannan, a specific plasma marker in Aspergillus infection) for an individual patient. The AUC:EC50 ratio can provide an in vivo estimate of drug response by linking drug exposure with PD.47,48 Acting as an in vivo measure of potency, AUC:EC50 enables an estimate of the host immune response to the invading organism. This has the potential to circumvent the problems associated with in vitro MIC estimation and may provide data that can drive the development of real-time algorithms for the delivery and control of individualized antimicrobial therapy. With the clinical validation of tools such as the AUC:EC50 for predicting antimicrobial PD in individuals using markers such as CRP, future work must now explore the role of using newer infection-related biomarkers, such as procalcitonin and CD64 for improving the accuracy of these tools. Furthermore, exploration of similar methods for predicting toxicity (e.g. renal toxicity) may further enhance the individualization of therapy by including host, antimicrobial agent, and pathogen factors in estimations of the outcome of therapy. Drug delivery Whilst intravenous and oral delivery of agents, via infusion pump and personalized dosing alerts respectively, may be the initial routes for antimicrobial delivery using such control systems there is also the potential for delivery via microneedle systems in the future. Such microneedles are now under investigation for drug and vaccine delivery that provide dual functions of sensing and also drug delivery.52 However, in the field of infection, the rate of drug delivery that can be achieved may be hindered by certain drug characteristics (such as hydrophilic versus hydrophobic agents) and the volume of agent required to be delivered. However, this technology may pose an interesting avenue for certain challenging cohorts, such as paediatric patients, as well as for local antimicrobial therapy delivery, such as skin and soft tissue infections or penetration of collections. Conclusions Novel systems are urgently required to individualize delivery of antimicrobial therapy, to address the wide variations in PK currently observed across a range of patient populations, and minimize the impact of sub-optimal dosing on clinical outcomes and AMR. Closed-loop control utilizing dermal antimicrobial sensing techniques offers a potential new avenue of applied research that addresses many of the current barriers associated with drug monitoring and dose optimization tools. Furthermore, the nature of minimally invasive sensor technology provides a platform that can be used across a range of settings from the community to those in intensive care. To achieve this there must be cross-disciplinary collaboration to explore the utility of such technologies to optimize the precision of antimicrobial therapy by addressing a number of the hurdles that remain to implementing this type of technology. Acknowledgements We thank the National Institute of Health Research Imperial Biomedical Research Centre and the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London in partnership with Public Health England and the NIHR Imperial Patient Safety Translational Research Centre. Funding This report is independent research funded by the National Institute for Health Research Invention for Innovation Grant (i4i), Enhanced, Personalized and Integrated Care for Infection Management at Point of Care (EPIC IMPOC), II-LA-0214-20008. A. C. G. is an NIHR Research Professor. This report was also supported by grants from (i) the Engineering, Medicine, Natural Sciences and Physical Sciences Bridging Research in Antimicrobial resistance: Collaboration and Exchange (EMBRACE), Imperial College Antimicrobial Research Collaborative; and (ii) Imperial College Biomedical Research Centre (BRC). J. A. R. wishes to recognize funding from the Australian National Health and Medical Research Council for Centre of Research Excellence (APP1099452) and a Practitioner Fellowship (APP1117065). Transparency declarations None to declare. Author contributions All authors contributed significantly to the literature review and writing of the manuscript. T. M. R. wrote the initial draft of the manuscript with all authors significantly contributing to the development and finalization of the version for submission. Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the UK Department of Health. References 1 O’Neill J. 2016. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations. https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf. 2 Holmes AH, Moore LSP, Sundsfjord A et al.   Understanding the mechanisms and drivers of antimicrobial resistance. Lancet  2016; 387: 176– 87. Google Scholar CrossRef Search ADS   3 Roberts JA, Abdul-Aziz MH, Lipman J et al.   Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions. Lancet Infect Dis  2014; 14: 498– 509. Google Scholar CrossRef Search ADS   4 Perez F, El Chakhtoura NG, Papp-Wallace K et al.   Treatment options for infections caused by carbapenem-resistant Enterobacteriaceae: can we apply ‘precision medicine’ to antimicrobial chemotherapy? Expert Opin Pharmacother  2016; 17: 761– 81. Google Scholar CrossRef Search ADS   5 Abdul-Aziz MH, Roberts JA, Lipman J et al.   Applying pharmacokinetic/pharmacodynamic principles in critically ill patients: optimizing efficacy and reducing resistance development. Semin Respir Crit Care Med  2015; 36: 136– 53. Google Scholar CrossRef Search ADS   6 Cotta MO, Roberts JA, Lipman J. We need to optimize piperacillin-tazobactam dosing in critically ill patients—but how? Crit Care  2016; 20: 163. Google Scholar CrossRef Search ADS   7 Brusselaers N, Vogelaers D, Blot S. The rising problem of antimicrobial resistance in the intensive care unit. Ann Intensive Care  2011; 1: 47. Google Scholar CrossRef Search ADS   8 Charmillon A, Novy E, Agrinier N et al.   The ANTIBIOPERF study: a nationwide cross-sectional survey about practices for β-lactam administration and therapeutic drug monitoring among critically ill patients in France. Clin Microbiol Infect  2016; 22: 625– 31. Google Scholar CrossRef Search ADS   9 Gonçalves-Pereira J, Póvoa P. Antibiotics in critically ill patients: a systematic review of the pharmacokinetics of β-lactams. Crit Care  2011; 15: R206. Google Scholar CrossRef Search ADS   10 Huttner A, Harbarth S, Hope WW et al.   Therapeutic drug monitoring of the β-lactam antibiotics: what is the evidence and which patients should we be using it for? J Antimicrob Chemother  2015; 70: 3178– 83. 11 Patel BM, Paratz J, See NC et al.   Therapeutic drug monitoring of β-lactam antibiotics in burns patients–a one-year prospective study. Ther Drug Monit  2012; 34: 160– 4. Google Scholar CrossRef Search ADS   12 Roberts JA, Norris R, Paterson DL et al.   Therapeutic drug monitoring of antimicrobials. Br J Clin Pharmacol  2012; 73: 27– 36. Google Scholar CrossRef Search ADS   13 Roberts JA, Paul SK, Akova M et al.   DALI: Defining antibiotic levels in intensive care unit patients: Are current β-lactam antibiotic doses sufficient for critically ill patients? Clin Infect Dis  2014; 58: 1072– 83. Google Scholar CrossRef Search ADS   14 Felton TW, Roberts JA, Lodise TP et al.   Individualization of piperacillin dosing for critically ill patients: Dosing software to optimize antimicrobial therapy. Antimicrob Agents Chemother  2014; 58: 4094– 102. Google Scholar CrossRef Search ADS   15 Carlier M, Stove V, Wallis SC et al.   Assays for therapeutic drug monitoring of β-lactam antibiotics: a structured review. Int J Antimicrob Agents  2015; 46: 367– 75. Google Scholar CrossRef Search ADS   16 Souza MJ, Kulmann RR, Silva LM et al.   Development and in-house validation of a microbiological assay for determination of cefepime in injectable preparations. J AOAC Int  2006; 89: 1367– 72. 17 Pickering MK, Brown SD. Assays for determination of ertapenem for applications in therapeutic drug monitoring, pharmacokinetics and sample stability. Biomed Chromatogr  2014; 28: 1525– 31. Google Scholar CrossRef Search ADS   18 Miranda Bastos AC, Vandecasteele SJ, Tulkens PM et al.   Development and validation of a high performance liquid chromatography assay for the determination of temocillin in serum of haemodialysis patients. J Pharm Biomed Anal  2014; 90: 192– 7. Google Scholar CrossRef Search ADS   19 Zalewski P, Cielecka-Piontek J, Paczkowska M. Development and validation of stability-indicating HPLC method for simultaneous determination of meropenem and potassium clavulanate. Acta Pol Pharm  2014; 71: 255– 60. 20 Wolff F, Deprez G, Seyler L et al.   Rapid quantification of six β-lactams to optimize dosage regimens in severely septic patients. Talanta  2013; 103: 153– 60. Google Scholar CrossRef Search ADS   21 Rawson TM, Moore LS, Hernandez B et al.   A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clin Microbiol Infect  2017; 23: 524– 32. Google Scholar CrossRef Search ADS   22 Wilhelm AJ, den Burger JCG, Swart EL. Therapeutic drug monitoring by dried blood spot: progress to date and future directions. Clin Pharmacokinet  2014; 53: 961– 73. Google Scholar CrossRef Search ADS   23 Chong H. Paper-based microfluidic point-of-care diagnostic devices for monitoring drug metabolism. J Nanomed Biotherapeut Discov  2013; 3: 1– 2. Google Scholar CrossRef Search ADS   24 Kang JS, Lee MH. Overview of therapeutic drug monitoring. Korean J Intern Med  2009; 24: 1– 10. Google Scholar CrossRef Search ADS   25 Parker SL, Dorofaeff T, Lipman J et al.   Is there a role for microsampling in antibiotic pharmacokinetic studies? Expert Opin Drug Metab Toxicol  2016; 5255: 601– 14. Google Scholar CrossRef Search ADS   26 Rawson TM, Sharma S, Georgiou P et al.   Towards a Minimally Invasive Device for β-lactam Monitoring in Humans. 2017. http://ac.els-cdn.com/S1388248117301856/1-s2.0-S1388248117301856-main.pdf?_tid=8511cda4-7048-11e7-976d-00000aab0f02&acdnat=1500884423_b35db9176145fbd9ccc62d4a0c7c1c04. 27 Reddy M, Herrero P, Sharkawy ME et al.   Metabolic control with the bio-inspired artificial pancreas in adults with type 1 diabetes: a 24-hour randomized controlled crossover study. J Diabetes Sci Technol  2016; 10: 405– 13. Google Scholar CrossRef Search ADS   28 Sharma S, Huang Z, Rogers M et al.   Evaluation of a minimally invasive glucose biosensor for continuous tissue monitoring. Anal Bioanal Chem  2016; 408: 8427– 35. Google Scholar CrossRef Search ADS   29 Yan G, Warner KS, Zhang J et al.   Evaluation needle length and density of microneedle arrays in the pretreatment of skin for transdermal drug delivery. Int J Pharm  2010; 391: 7– 12. Google Scholar CrossRef Search ADS   30 Bariya SH, Gohel MC, Mehta TA et al.   Microneedles: an emerging transdermal drug delivery system. J Pharm Pharmacol  2012; 64: 11– 29. Google Scholar CrossRef Search ADS   31 Trevitt S, Simpson S, Wood A. Artificial pancreas device systems for the closed-loop control of type 1 diabetes: what systems are in development? J Diabetes Sci Technol  2016; 10: 714– 23. Google Scholar CrossRef Search ADS   32 Absalom AR, Sutcliffe N, Kenny GN. Closed-loop control of anesthesia using bispectral index. Anesthesiology  2002; 96: 67– 73. Google Scholar CrossRef Search ADS   33 Madhavan JS, Puri GD, Mathew PJ. Closed-loop isoflurane administration with bispectral index in open heart surgery: randomized controlled trial with manual control. Acta Anaesthesiol Taiwan  2011; 49: 130– 5. Google Scholar CrossRef Search ADS   34 El-Laboudi A, Oliver NS, Cass A et al.   Use of microneedle array devices for continuous glucose monitoring: a review. Diabetes Technol Ther  2013; 15: 101– 15. Google Scholar CrossRef Search ADS   35 Trzebinski J, Sharma S, Radomska-Botelho Moniz A et al.   Microfluidic device to investigate factors affecting performance in biosensors designed for transdermal applications. Lab Chip  2012; 12: 348. Google Scholar CrossRef Search ADS   36 Roberts JA, Udy AA, Jarrett P et al.   Plasma and target-site subcutaneous tissue population pharmacokinetics and dosing simulations of cefazolin in post-trauma critically ill patients. J Antimicrob Chemother  2014; 70: 1495– 502. Google Scholar CrossRef Search ADS   37 Johnson BL, Damer KM, Walroth TA et al.   A systematic review of vancomycin dosing and monitoring in burn patients. J Burn Care Res  2015; 36: 641– 50. Google Scholar CrossRef Search ADS   38 Roberts JA, Roberts MS, Robertson TA et al.   Piperacillin penetration into tissue of critically ill patients with sepsis—bolus versus continuous administration? Crit Care Med  2009; 37: 926– 33. Google Scholar CrossRef Search ADS   39 Vincent J-L, Bassetti M, François B et al.   Advances in antibiotic therapy in the critically ill. Crit Care  2016; 20: 133. Google Scholar CrossRef Search ADS   40 Joukhadar C, Frossard M, Mayer BX et al.   Impaired target site penetration of β-lactams may account for therapeutic failure in patients with septic shock. Crit Care Med  2001; 29: 385– 91. Google Scholar CrossRef Search ADS   41 Johnson MA, Moradi MH, eds. PID Control . London: Springer, 2005. http://link.springer.com/10.1007/1-84628-148-2. Google Scholar CrossRef Search ADS   42 Ahn H-S, Chen Y, Moore KL. Iterative Learning Control . London: Springer London, 2007. http://link.springer.com/10.1007/978-1-84628-859-3. Google Scholar CrossRef Search ADS   43 Wang Y, Gao F, Doyle FJ. Survey on iterative learning control, repetitive control, and run-to-run control. J Process Control  2009; 19: 1589– 600. Google Scholar CrossRef Search ADS   44 Madady A. PID type iterative learning control with optimal gains. Int J Control Autom Syst  2008; 6: 194– 203. 45 Nielsen EI, Friberg LE. Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Pharmacol Rev  2013; 65: 1053– 90. Google Scholar CrossRef Search ADS   46 Frimodt-Møller N. How predictive is PK/PD for antibacterial agents? Int J Antimicrob Agents  2002; 19: 333– 9. Google Scholar CrossRef Search ADS   47 Huurneman LJ, Neely M, Veringa A et al.   Pharmacodynamics of voriconazole in children: Further steps along the path to true individualized therapy. Antimicrob Agents Chemother  2016; 60: 2336– 42. Google Scholar CrossRef Search ADS   48 Ramos-Martín V, Neely MN, McGowan P et al.   Population pharmacokinetics and pharmacodynamics of teicoplanin in neonates: making better use of C-reactive protein to deliver individualized therapy. J Antimicrob Chemother  2016; 71: 3168– 78. Google Scholar CrossRef Search ADS   49 Henry S, McAllister DV, Allen MG et al.   Microfabricated microneedles: a novel approach to transdermal drug delivery. J Pharm Sci  1998; 87: 922– 5. Google Scholar CrossRef Search ADS   50 Sharma S, Saeed A, Johnson C et al.   Rapid, low cost prototyping of transdermal devices for personal healthcare monitoring. Sens Biosensing Res  2017; 13: 104– 8. Google Scholar CrossRef Search ADS   51 Moniz ARB, Michelakis K, Trzebinski J et al.   Minimally invasive enzyme microprobes: an alternative approach for continuous glucose monitoring. J Diabetes Sci Technol  2012; 6: 479– 80. Google Scholar CrossRef Search ADS   52 Kim YC, Park JH, Prausnitz MR. Microneedles for drug and vaccine delivery. Adv Drug Deliv Rev  2012; 64: 1547– 68. Google Scholar CrossRef Search ADS   53 Ranamukhaarachchi SA, Padeste C, Dübner M et al.   Integrated hollow microneedle-optofluidic biosensor for therapeutic drug monitoring in sub-nanoliter volumes. Sci Rep  2016; 6: 29075. Google Scholar CrossRef Search ADS   54 Ferguson BS, Hoggarth DA, Maliniak D et al.   Real-time, aptamer-based tracking of circulating therapeutic agents in living animals. Sci Transl Med  2013; 5: 213ra165. Google Scholar CrossRef Search ADS   55 Lee H, Song C, Hong YS et al.   Wearable/disposable sweat-based glucose monitoring device with multistage transdermal drug delivery module. Sci Adv  2017; 3: e1601314. Google Scholar CrossRef Search ADS   56 Hayat A, Marty JL. Aptamer based electrochemical sensors for emerging environmental pollutants. Front Chem  2014; 2: 41. Google Scholar CrossRef Search ADS   57 Gorchkov DV, Soldatkin AP, Maupas H et al.   Correlation between the electrical charge properties of polymeric membranes and the characteristics of ion field effect transistors or penicillinase based enzymatic field effect transistors. Anal Chim Acta  1996; 331: 217– 23. Google Scholar CrossRef Search ADS   58 Healey BG, Walt DR. Improved fiber-optic chemical sensor for penicillin. Anal Chem  1995; 67: 4471– 6. Google Scholar CrossRef Search ADS   59 Lee SR, Rahman MM, Sawada K et al.   Fabrication of a highly sensitive penicillin sensor based on charge transfer techniques. Biosens Bioelectron  2009; 24: 1877– 82. Google Scholar CrossRef Search ADS   60 O’Hare D. Biosensors and sensor systems. In: Yang G, ed. Body Sensor Networks . Springer, 2014. http://link.springer.com/10.1007/978-1-4471-6374-9. 61 Trouillon R, Combs Z, Patel BA et al.   Comparative study of the effect of various electrode membranes on biofouling and electrochemical measurements. Electrochem Commun  2009; 11: 1409– 13. Google Scholar CrossRef Search ADS   62 Herrero P, Georgiou P, Oliver N et al.   A bio-inspired glucose controller based on pancreatic β-cell physiology. J Diabetes Sci Technol  2012; 6: 606– 16. Google Scholar CrossRef Search ADS   63 Philip A, Rawson TM, Moore LSP et al.   Closed-loop controller systems for the precision delivery of vancomycin: an in silico proof-of-concept. In: Federation of Infection Society Annual Conference, 2016. Abstract 5121. http://www.journalofhospitalinfection.com/article/S0195-6701(16)30516-3/pdf. 64 Ulldemolins M, Vaquer S, Llauradó-Serra M et al.   β-Lactam dosing in critically ill patients with septic shock and continuous renal replacement therapy. Crit Care  2014; 18: 227. Google Scholar CrossRef Search ADS   65 Cataldo MA, Tacconelli E, Grilli E et al.   Continuous versus intermittent infusion of vancomycin for the treatment of Gram-positive infections: systematic review and meta-analysis. J Antimicrob Chemother  2012; 67: 17– 24. Google Scholar CrossRef Search ADS   66 Wysocki M, Delatour F, Faurisson F et al.   Continuous versus intermittent infusion of vancomycin in severe staphylococcal infections: prospective multicenter randomized study. Antimicrob Agents Chemother  2001; 45: 2460– 7. Google Scholar CrossRef Search ADS   67 Ulldemolins M, Martin-Loeches I, Llaurado-Serra M et al.   Piperacillin population pharmacokinetics in critically ill patients with multiple organ dysfunction syndrome receiving continuous venovenous haemodiafiltration: effect of type of dialysis membrane on dosing requirements. J Antimicrob Chemother  2016; 71: 1651– 9. Google Scholar CrossRef Search ADS   68 Roberts JA, Lipman J, Blot S et al.   Better outcomes through continuous infusion of time-dependent antibiotics to critically ill patients? Curr Opin Crit Care  2008; 14: 390– 6. Google Scholar CrossRef Search ADS   69 Roos JF, Lipman J, Kirkpatrick CMJ. Population pharmacokinetics and pharmacodynamics of cefpirome in critically ill patients against Gram-negative bacteria. Intensive Care Med  2007; 33: 781– 8. Google Scholar CrossRef Search ADS   70 Ramos-Martín V, Johnson A, Livermore J et al.   Pharmacodynamics of vancomycin for CoNS infection: experimental basis for optimal use of vancomycin in neonates. J Antimicrob Chemother  2016; 992– 1002. 71 Udy AA, Lipman J, Jarrett P et al.   Are standard doses of piperacillin sufficient for critically ill patients with augmented creatinine clearance? Crit Care  2015; 19: 28. Google Scholar CrossRef Search ADS   72 Chagnac A, Weinstein T, Korzets A et al.   Glomerular hemodynamics in severe obesity. Am J Physiol Renal Physiol  2000; 278: F817– 22. Google Scholar CrossRef Search ADS   73 Shimamoto Y, Fukuda T, Tanaka K et al.   Systemic inflammatory response syndrome criteria and vancomycin dose requirement in patients with sepsis. Intensive Care Med  2013; 39: 1247– 52. Google Scholar CrossRef Search ADS   74 Blot SI, Pea F, Lipman J. The effect of pathophysiology on pharmacokinetics in the critically ill patient - Concepts appraised by the example of antimicrobial agents. Adv Drug Deliv Rev  2014; 77: 3– 11. Google Scholar CrossRef Search ADS   75 Herrero P, Pesl P, Reddy M et al.   Advanced insulin bolus advisor based on run-to-run control and case-based reasoning. IEEE J Biomed Heal Informatics  2015; 19: 1087– 96. 76 Phua J, Ngerng W, See K et al.   Characteristics and outcomes of culture-negative versus culture-positive severe sepsis. Crit Care  2013; 17: R202. Google Scholar CrossRef Search ADS   77 Zarb P, Goossens H. European Surveillance of Antimicrobial Consumption (ESAC): value of a point-prevalence survey of antimicrobial use across Europe. Drugs  2011; 71: 745– 55. Google Scholar CrossRef Search ADS   78 Rawson TM, Charani E, Moore LSP et al.   Mapping the decision pathways of acute infection management in secondary care among UK medical physicians: a qualitative study. BMC Med  2016; 14: 208. Google Scholar CrossRef Search ADS   79 de Jong E, van Oers JA, Beishuizen A et al.   Efficacy and safety of procalcitonin guidance in reducing the duration of antibiotic treatment in critically ill patients: a randomised, controlled, open-label trial. Lancet Infect Dis  2016; 16: 819– 27. Google Scholar CrossRef Search ADS   80 Markanday A. Acute phase reactants in infections: evidence-based review and a guide for clinicians. Open Forum Infect Dis  2015; 2: ofv098. Google Scholar CrossRef Search ADS   81 Kobeissi ZA, Zanotti-Cavazzoni SL. Biomarkers of sepsis. Yearb Crit Care Med  2010; 2010: 227– 8. Google Scholar CrossRef Search ADS   82 Nargis W, Ahamed B, Ibrahim M. Procalcitonin versus C-reactive protein: usefulness as biomarker of sepsis in ICU patient. Int J Crit Illn Inj Sci  2014; 4: 195. Google Scholar CrossRef Search ADS   83 Nagaraj VJ, Jacobs M, Vattipalli KM et al.   Nanochannel-based electrochemical sensor for the detection of pharmaceutical contaminants in water. Environ Sci Process Impacts  2013; 16: 135– 40. Google Scholar CrossRef Search ADS   84 Zhang K, Lu L, Wen Y et al.   Facile synthesis of the necklace-like graphene oxide-multi-walled carbon nanotube nanohybrid and its application in electrochemical sensing of Azithromycin. Anal Chim Acta  2013; 787: 50– 6. Google Scholar CrossRef Search ADS   85 Pinacho DG, Sánchez-Baeza F, Pividori MI et al.   Electrochemical detection of fluoroquinolone antibiotics in milk using a magneto immunosensor. Sensors (Basel)  2014; 14: 15965– 80. Google Scholar CrossRef Search ADS   86 Gayen P, Chaplin BP. Selective electrochemical detection of ciprofloxacin with a porous nafion/multiwalled carbon nanotube composite film electrode. ACS Appl Mater Interfaces  2016; 8: 1615– 26. Google Scholar CrossRef Search ADS   87 Theanponkrang S, Suginta W, Weingart H et al.   Robotic voltammetry with carbon nanotube-based sensors: a superb blend for convenient high-quality antimicrobial trace analysis. Int J Nanomedicine  2015; 10: 859– 68. 88 Zhang F, Gu S, Ding Y et al.   A novel sensor based on electropolymerization of β-cyclodextrin and l-arginine on carbon paste electrode for determination of fluoroquinolones. Anal Chim Acta  2013; 770: 53– 61. Google Scholar CrossRef Search ADS   89 Giroud F, Gorgy K, Gondran C et al.   Impedimetric immunosensor based on a polypyrrole-antibiotic model film for the label-free picomolar detection of ciprofloxacin. Anal Chem  2009; 81: 8405– 9. Google Scholar CrossRef Search ADS   90 Moraes FC, Silva TA, Cesarino I et al.   Antibiotic detection in urine using electrochemical sensors based on vertically aligned carbon nanotubes. Electroanalysis  2013; 25: 2092– 9. Google Scholar CrossRef Search ADS   91 Zhang X, Zhang Y-C, Zhang J-W. A highly selective electrochemical sensor for chloramphenicol based on three-dimensional reduced graphene oxide architectures. Talanta  2016; 161: 567– 73. Google Scholar CrossRef Search ADS   92 Jakubec P, Urbanová V, Medříková Z et al.   Advanced sensing of antibiotics with magnetic gold nanocomposite: electrochemical detection of chloramphenicol. Chemistry  2016; 22: 14279– 84. Google Scholar CrossRef Search ADS   93 Govindasamy M, Chen SM, Mani V et al.   Molybdenum disulfide nanosheets coated multiwalled carbon nanotubes composite for highly sensitive determination of chloramphenicol in food samples milk, honey and powdered milk. J Colloid Interface Sci  2017; 485: 129– 36. Google Scholar CrossRef Search ADS   94 Karthik R, Govindasamy M, Chen SM et al.   Green synthesized gold nanoparticles decorated graphene oxide for sensitive determination of chloramphenicol in milk, powdered milk, honey and eye drops. J Colloid Interface Sci  2016; 475: 46– 56. Google Scholar CrossRef Search ADS   95 Meenakshi S, Pandian K, Jayakumari LS et al.   Enhanced amperometric detection of metronidazole in drug formulations and urine samples based on chitosan protected tetrasulfonated copper phthalocyanine thin-film modified glassy carbon electrode. Mater Sci Eng C Mater Biol Appl  2016; 59: 136– 44. Google Scholar CrossRef Search ADS   96 Gan T, Shi Z, Sun J, Liu Y. Simple and novel electrochemical sensor for the determination of tetracycline based on iron/zinc cations-exchanged montmorillonite catalyst. Talanta  2014; 121: 187– 93. Google Scholar CrossRef Search ADS   97 Liu S, Wang Y, Xu W et al.   A novel sandwich-type electrochemical aptasensor based on GR-3D Au and aptamer-AuNPs-HRP for sensitive detection of oxytetracycline. Biosens Bioelectron  2017; 88: 181– 7. Google Scholar CrossRef Search ADS   98 Rastgar S, Shahrokhian S. Nickel hydroxide nanoparticles-reduced graphene oxide nanosheets film: Layer-by-layer electrochemical preparation, characterization and rifampicin sensory application. Talanta  2014; 119: 156– 63. Google Scholar CrossRef Search ADS   99 Janata J, City SL. pH-based enzyme potentiometric sensors. Anal Chem  1985; 57: 1924– 5. Google Scholar CrossRef Search ADS   100 Anzai J, Hashimoto J, Osa T et al.   Penicillin sensors based on an ion-sensitive coated with stearic acid Langmuir-Blodgett field effect membrane transistor. Anal Sci  1988; 4: 247– 50. Google Scholar CrossRef Search ADS   101 Yerian TD, Christian GD, Ruzicka J. Flow injection analysis as a diagnostic technique for development and testing of chemical sensors. Anal Chim Acta  1988; 204: 7– 28. Google Scholar CrossRef Search ADS   102 Gao X, Zhen R, Zhang Y, Grimes CA. Detecting penicillin in milk with a wireless magnetoelastic biosensor. Sen Lett  2009; 7: 6– 10. Google Scholar CrossRef Search ADS   103 Wang H, Wang Y, Liu S et al.   Signal-on electrochemical detection of antibiotics at zeptomole level based on target-aptamer binding triggered multiple recycling amplification. Biosens Bioelectron  2016; 80: 471– 6. Google Scholar CrossRef Search ADS   104 Daprà J, Lauridsen LH, Nielsen AT et al.   Comparative study on aptamers as recognition elements for antibiotics in a label-free all-polymer biosensor. Biosens Bioelectron  2013; 43: 315– 20. Google Scholar CrossRef Search ADS   105 Pikkemaat MG. Microbial screening methods for detection of antibiotic residues in slaughter animals. Anal Bioanal Chem  2009; 395: 893– 905. Google Scholar CrossRef Search ADS   106 Huet AC, Delahaut P, Fodey T et al.   Advances in biosensor-based analysis for antimicrobial residues in foods. Trends Anal Chem  2010; 29: 1281– 94. Google Scholar CrossRef Search ADS   107 Willander M, Khun K, Ibupoto ZH. Metal oxide nanosensors using polymeric membranes, enzymes and antibody receptors as ion and molecular recognition elements. Sensors (Switzerland)  2014; 14: 8605– 32. Google Scholar CrossRef Search ADS   108 Ismail F, Adeloju SB. Galvanostatic entrapment of penicillinase into polytyramine films and its utilization for the potentiometric determination of penicillin. Sensors  2010; 10: 2851– 68. Google Scholar CrossRef Search ADS   109 Bi X, Hartono D, Yang KL. Real-time liquid crystal pH sensor for monitoring enzymatic activities of penicillinase. Adv Funct Mater  2009; 19: 3760– 5. Google Scholar CrossRef Search ADS   110 Gonçalves LM, Callera WFA, Sotomayor MDPT et al.   Penicillinase-based amperometric biosensor for penicillin G. Electrochem Commun  2014; 38: 131– 3. Google Scholar CrossRef Search ADS   111 Müntze GM, Baur B, Schäfer W et al.   Quantitative analysis of immobilized penicillinase using enzyme-modified AlGaN/GaN field-effect transistors. Biosens Bioelectron  2015; 64: 605– 10. Google Scholar CrossRef Search ADS   112 Siqueira JR, Abouzar MH, Poghossian A et al.   Penicillin biosensor based on a capacitive field-effect structure functionalized with a dendrimer/carbon nanotube multilayer. Biosens Bioelectron  2009; 25: 497– 501. Google Scholar CrossRef Search ADS   113 Cháfer-Pericás C, Maquieira Á, Puchades R. Fast screening methods to detect antibiotic residues in food samples. Trends Anal Chem  2010; 29: 1038– 49. Google Scholar CrossRef Search ADS   114 Rowe AA, Miller EA, Plaxco KW. Reagentless measurement of aminoglycoside antibiotics in blood serum via an electrochemical, ribonucleic acid aptamer-based biosensor. Anal Chem  2010; 82: 7090– 5. Google Scholar CrossRef Search ADS   115 Wu X, Kuang H, Hao C et al.   Paper supported immunosensor for detection of antibiotics. Biosens Bioelectron  2012; 33: 309– 12. Google Scholar CrossRef Search ADS   116 Schoukroun-Barnes LR, Wagan S, White RJ. Enhancing the analytical performance of electrochemical RNA aptamer-based sensors for sensitive detection of aminoglycoside antibiotics. Anal Chem  2014; 86: 1131– 7. Google Scholar CrossRef Search ADS   117 Han S, Li B, Song Z et al.   A kanamycin sensor based on an electrosynthesized molecularly imprinted poly-o-phenylenediamine film on a single-walled carbon nanohorn modified glassy carbon electrode. Analyst  2016; 142: 218– 23. Google Scholar CrossRef Search ADS   118 Nikolaus N, Strehlitz B. DNA-aptamers binding aminoglycoside antibiotics. Sensors (Basel)  2014; 14: 3737– 55. Google Scholar CrossRef Search ADS   119 Chiu M-H, Yang H-H, Liu C-H et al.   Determination of lincomycin in urine and some foodstuffs by flow injection analysis coupled with liquid chromatography and electrochemical detection with a preanodized screen-printed carbon electrode. J Chromatogr B Analyt Technol Biomed Life Sci  2009; 877: 991– 4. Google Scholar CrossRef Search ADS   120 Zacco E, Adrian J, Galve R et al.   Electrochemical magneto immunosensing of antibiotic residues in milk. Biosens Bioelectron  2007; 22: 2184– 91. Google Scholar CrossRef Search ADS   121 Joseph R, Girish Kumar K. Differential pulse voltammetric determination and catalytic oxidation of sulfamethoxazole using [5,10,15,20- tetrakis (3-methoxy-4-hydroxy phenyl) porphyrinato] Cu (II) modified carbon paste sensor. Drug Test Anal  2010; 2: 278– 83. Google Scholar CrossRef Search ADS   122 Sgobbi LF, Razzino CA, Machado SAS. A disposable electrochemical sensor for simultaneous detection of sulfamethoxazole and trimethoprim antibiotics in urine based on multiwalled nanotubes decorated with Prussian blue nanocubes modified screen-printed electrode. Electrochim Acta  2016; 191: 1010– 7. Google Scholar CrossRef Search ADS   © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Journal

Journal of Antimicrobial ChemotherapyOxford University Press

Published: Apr 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial