International Journal of Pharmaceutics 544 (2018) 319–334

Contents lists available at ScienceDirect

International Journal of Pharmaceutics
journal homepage: www.elsevier.com/locate/ijpharm

A review of implantable biosensors for closed-loop glucose control and otherdrug delivery applications
Kee Scholten, Ellis MengT⁎

Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1111, USA

A R T I C L E I N F O

A B S T R A C T

Keywords:
Closed-loop drug delivery
Biosensor
Implantable sensor
Continuous glucose monitoring
Enzymatic sensor

Closed-loop drug delivery promises autonomous control of pharmacotherapy through the continuous monitoringof biomarker levels. For decades, researchers have strived for portable closed-loop systems capable of treating
ambulatory patients with chronic conditions such as diabetes mellitus. After years of development, the ﬁrst ofthese systems have left the laboratory and entered commercial use. This long-awaited advance reﬂects recent
development of chronically stable implantable biosensors able to accurately measure biomarker levels in vivo.
This review discusses the role of implantable biosensors in closed-loop drug delivery applications, with the intent
to provide a resource for engineers and researchers studying such systems. We provide an overview of common
biosensor designs and review the principle challenges in implementing long indwelling sensors: namely device
sensitivity, selectivity, and lifetime. This review examines novel advances in transducer design, biological interface, and material biocompatibility, with a focus on recent academic and commercial work which provide
successful strategies to overcome perennial challenges. This review focuses primarily on the topics of closed-loop
glucose control and continuous glucose monitoring biosensors, which make up the overwhelming majority of
published research in this area. We conclude with an overview of recent advances in closed-loop systems targeting applications outside blood glucose management.

1. Introduction
Closed-loop drug delivery is a fully automated method for regulating pharmacotherapy using a self-controlled feedback system. A
diagram of a theoretical system is presented in Fig. 1 highlighting thethree principal components: (i) an implanted biosensor which monitors
physiological condition, circulating drug concentration, or biomarker
level; (ii) a control system which receives input from the sensor andprovides medication dosing instructions; and (iii) a drug delivery device
which provides pharmacotherapy with the goal of creating a measurable change in patient-condition as monitored by the sensor (closing theloop). Semi-closed-loop systems, which require some degree of user
input, have been present in the practice of anesthesiology for years; aphysiological sensor monitors breathing rate and/or blood gas concentrations, and continuously informs the anesthesiologist who oversees delivery of anesthesia (Miller and Gan, 2013). Bedside systems for
temporary closed-loop delivery of insulin, featuring continuous blood
glucose monitoring, were introduced in 1974 (Albisser et al., 1974) andcommercialized in 1977 (Clemens et al., 1977). For decades, researchers have hypothesized a wearable or implantable system fortreating chronic conditions - an idea borne out of continuous advances⁎in medical device miniaturization and automation. Consider a microscopic implantable sensor continuously monitoring chemical biomarkers, a pocket computer able to interpret the data and conﬁdently
make treatment decisions, and a personal drug-infusion pump able toprecisely infuse the patient with medication. Such a system would oﬀer
more than convenience; it would fundamentally change how many
conditions are treated. Medication could be maintained at precisely therequired levels for optimal therapeutic eﬀect over long durations.
Closed-loop systems could respond immediately, or even preemptively,
to sudden deterioration in condition. Perennial problems of patientadherence, physician error, and over- or under-medication could bealmost entirely eliminated.
Several of the requisite technologies for such closed-loop control
have reached maturation. The proliferation of smartphones has madepowerful, ubiquitous computers a reality, and wearable drug-infusion
pumps are commercially available for treatment of conditions such astype 1 diabetes mellitus. However, despite considerable interest and intensive eﬀort, complete closed-loop systems have remained hypothetical, with the development of a viable and reliable sensor posing thelargest obstacle.
This long abeyance appears to have ended. In recent years, a ﬂurry

Corresponding author.
E-mail address: ellis.meng@usc.edu (E. Meng).

https://doi.org/10.1016/j.ijpharm.2018.02.022
Received 6 September 2017; Received in revised form 30 January 2018; Accepted 15 February 2018
Available online 16 February 20180378-5173/ © 2018 Elsevier B.V. All rights reserved.

International Journal of Pharmaceutics 544 (2018) 319–334

K. Scholten, E. Meng

2. Overview of a closed-loop drug delivery system
2.1. Implantable biosensor
A biosensor is a tool which detects the presence of a speciﬁc chemical or biological molecule and outputs a physical signal in proportion
to the amount or concentration of the target. A biosensor relies on abiological component, sometimes called a biological recognition element, to identify, trap, or bind with the target analyte. The use of abiological component distinguishes biosensors from the broader ﬁeld ofchemical/biochemical sensors, and imparts high selectivity due to thespeciﬁc biological interaction of the analyte and the recognition element. For implanted sensors, which are exposed to the truly vast
number of proteins, metabolites, cells, and chemicals present in aphysiological environment, this high selectivity is absolutely crucial for
accurate operation. The archetypal biosensor has two basic components, the biological element which interacts with the target analyte,
and the physical transducer which interacts with the biological element
to convert the chemical interaction into a quantiﬁable signal.
Biosensors can be broadly classiﬁed into two categories, diﬀerentiatedby the type of biological component: bioaﬃnity sensors and biocatalytic or enzymatic sensors (Fig. 2).
A diagram of a generic bioaﬃnity sensor is presented in Fig. 2a.
Bioaﬃnity sensors consist of a physical transducer coated with a thinfunctionalization layer of ‘capture molecules’, which bind selectively
and typically irreversibly to target analytes. These capture molecules
represent the biological recognition element of a bioaﬃnity sensor, andmay be an immobilized ﬁlm of antibodies, antigens, aptamers, or DNA/RNA segments. Detection of an analyte is encoded in a transduction
signal, such as a measured change in mass, ﬁlm thickness, charge, dielectric constant, or local-ﬁeld potential due to the presence of thecaptured analyte, or the ﬂuorescent signal from an accompanying
ﬂuorophore tag. These devices are incredibly selective and sensitive
owing to the strong interaction between the biological component andthe target, and comprise the core technology of powerful ex vivo immunosensors. However, due to typically short lifetimes in vivo and theslow response times (relative to the high temporal resolution needed for
drug-delivery feedback), they are rarely used as implants and even lessfrequently in closed-loop research. Accordingly, we will discuss thesedevices sparingly in this review.
Enzymatic sensors use a biological enzyme to catalyze the reactionof the target into another species, and typically transduce theFig. 1. Diagram of a closed-loop drug delivery system featuring the three major components: implanted biosensor, control system, and drug pump.

of clinical studies have explored ad hoc closed-loop systems comprisinga mix of research and commercial devices (Bergenstal et al., 2016;
Kovatchev et al., 2014; Kropﬀ et al., 2015; Leelarathna et al., 2014;
Renard et al., 2016; Tauschmann et al., 2016; Thabit et al., 2014;
Thabit et al., 2015). Public and academic interest has soared, spurredby the Juveniles Diabetes Research Foundation’s mission to develop apermanent closed-loop therapy for diabetes patients. News reports haveemerged of inventive diabetes patients cobbling together home-brew
semi-closed-loop systems for insulin control using commercial insulin
pumps, glucose sensors, and personal microcomputers (Leigh, 2017;
Mann, 2016). Finally, in 2017, the ﬁrst purportedly closed-loop system
received FDA approval. The MiniMed® 670G (Medtronic PLC., Dublin,
Ireland) automatically modulates insulin delivery, albeit with limiteduser input still required, relying on an implantable glucose sensor tocontinuously measure blood sugar levels (FDA, 2016c). This sudden
progress is a consequence of major advances in implantable biomedical
technologies, and in particular, the development of a commerciallyavailable glucose biosensor implant. We believe further advances andproliferation of closed-loop drug delivery systems are impending, primarily as a consequence of further biosensor development.
Here we review the role of implantable biosensors for closed-loop
drug delivery platforms. We examine the principle challenges facing
biosensor development with a focus on the needs for long-indwellingdevices, namely: sensitivity, selectivity, and lifetime. We examine anddiscuss results from the recent literature, with an emphasis on novel
advances in transducer design, biological interface, and biocompatibility. Finally, we review the status of biosensor technology for current
and future closed-loop drug delivery applications, with a primary focus
on type 1 diabetes, which captivates the overwhelming majority ofbiosensor and closed-loop drug delivery system research.

Fig. 2. Diagram of (a) generic bioaﬃnity-type biosensor and (b) generic enzyme-based
amperometric biosensor.

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K. Scholten, E. Meng

accompanying production or consumption of a mediator, co-product, or
substrate. Enzymatic sensors are selective, owing to the speciﬁc actionof the biological enzyme and, unlike bioaﬃnity sensors, the biologicalelement is unchanged after detection. As such, enzymatic biosensors
can be used repeatedly and continuously, and make up the overwhelming majority of implantable biosensors. While both oxidase anddehydrogenase enzymes are commonly used in ex vivo biosensors, only
oxidases are typically used in vivo as there is an available supply of O2
to regenerate the enzyme. Though enzymatic sensors include thoseusing ﬂuorescent, plasmonic, and voltammetric transducers among
others, by far the most common are amperometric designs. Notably,both the ﬁrst biosensor (Clark and Lyons, 1962) and ﬁrst implanted
biosensor (Sternberg et al., 1989) were amperometric and enzymatic. A
diagram of a common amperometric biocatalytic sensor is presented inFig. 2b. The basic transducer comprises a set of electrodes held at aconstant potential diﬀerence, with the working electrode coated in alayer of enzyme. Analytes at the enzyme layer undergo a catalyzed
reaction and the enzyme is regenerated through the reduction of amediator compound, which may be naturally present in the body or coimmobilized with the enzyme. The reduced mediator is electrochemically oxidized at the working electrode, resulting in an electric
current (nA to µA) proportional to the local concentration of the target
analyte. Section 4.1.1 provides a more detailed examination of several
common enzymatic sensor designs for the speciﬁc case of glucose biosensors. The success of these devices can be attributed to the simplicityof the design, the selectivity and stability of commercial enzymes, andthe ease of integrating amperometric signals into electronic controllers.
In a closed-loop drug delivery system, the biosensor provides acontinuous measurement of the target analyte, which may be a biomarker indicative of an adverse condition or a direct measure of circulating drug concentration. Measurements are collected intermittently
and transmitted to the controller to inform rate of drug infusion. The
sensor must respond sensitively, accurately, and quickly to small ﬂuctuations in analyte concentration, and must be stable in vivo for chronic
use. These challenges are addressed in greater detail in Section 3.

delivery feature commercial smartphone control systems (Keith-Hynes
et al., 2014; Kovatchev et al., 2014; Renard et al., 2016), and both
Dexcom (San Diego, CA) and Abbott Laboratories (Lake Bluﬀ, IL) oﬀer
implantable glucose biosensors with smartphone integration, using theBluetooth and NFC protocols respectively (AirStrip Technologies, 2016;
Dexcom, 2017). The MiniMed® 670G instead integrates the control
system into the insulin pump, which is tethered to the patient by acatheter and communicates wirelessly with the implanted biosensor(Medtronic, 2017). This integrated approach removes the need for additional components, and reduces the risk that the control unit will
become separated from the user. Despite the critical role of the control
system, there is currently limited published research examining theeﬀect of diﬀerent designs on patient outcome.

2.2. Control system

3. Challenges in implantable biosensor design

The control system serves as the intermediary between the biosensor and the drug delivery mechanism, translating the sensor output
into instructions for pharmacological dosing. In practice, the control
system is a personal computer, either a purpose-built unit or a laptop or
smartphone running proprietary software. During operation, the control system converts the raw output of the biosensor to an estimate ofanalyte concentration using a pre-calibrated conversion factor, andmodulates drug infusion in accordance with an algorithm. Control algorithms can vary greatly in complexity; in the simplest approach, thecontroller simply activates drug infusion when the biosensor response
falls outside a preset window, and halts delivery when the response
returns to a desired value. More sophisticated algorithms attempt tomodel the dynamics of the target biomarker and the action of the drugfor precise therapeutic control. Researchers have proposed numerous
control schemes (Bequette, 2005), though there have been few clinicalstudies providing head-to-head comparisons of performance. Among
recently developed closed-loop systems model predictive control andproportional integral derivative control have emerged as popular
choices. In general, control algorithms demand modest computational
resources, regardless of speciﬁc design, and so choice of algorithm has
little eﬀect on the requirements for the physical control system.
The control system is typically worn or carried by the patient at all
times, and therefore the form-factor has great signiﬁcance for system
reliability and patient adherence. The ideal control system is small,lightweight, and unobtrusive, with a long battery life and wirelesscapabilities to obviate the need for wired connections to the drug pumpand biosensor. Smartphones are an appealing choice owing to theirubiquity and wireless features. Several studies of closed-loop glucose

3.1. Sensitivity

2.3. Drug delivery mechanism
The typical administration mechanism is a personal drug infusion
pump, a small, electronically controlled, mechanical pump which delivers pharmaceutical infusions to the user by way of a subcutaneous or
intravascular catheter. Drug infusion pumps represent the most maturetechnology of the closed-loop drug delivery system. Modern devices
feature low ﬂow rates (< µl/minute) achievable with high precision,
small form-factors, battery power, and wireless control (Mossman,2010). Commercial examples include portable and/or wearable insulin
pumps for diabetics and continuous infusion pumps for at-home chemotherapy. Researchers have adapted several commercial insulin
pumps for use in closed-loop control of blood glucose levels, demonstrating the systems as an eﬀective platform for future closed-loop research. Many current devices already feature wireless, app-based control, and require no major adaptation for integration into closed-loop
application. The majority of current closed-loop research features subcutaneous pumps due to the higher ease-of-use for patients, though they
induce a time-lag for which the control system must compensate.
Phelps (2011) provides a more expansive examination of current drugdelivery technology.

Sensitivity to the target analyte is the foremost requirement forbiosensor design. An implantable biosensor must respond to changes intarget analyte concentration at physiologically relevant values in thepresence of interfering species and, for closed-loop applications, this
response must be quantitative in order to properly titrate drug delivery.
For the purposes of this review we deﬁne sensitivity as the change insensor output for a given change in analyte concentration:S=f ([a])−f (0)∂f ([a])
≈
[a]
∂ [a]

(1)

where f is the function describing sensor output to analyte a, and theapproximation holds for biosensors with linear responses over the rangeof concentration of interest. Consequently the limit of detection (LOD),
or minimum resolution, of the sensor can be deﬁned as:

LOD =

3σ
S

(2)

assuming a sensor which responds linearly with analyte concentration
and a baseline noise characterized by a standard deviation σ. The LOD
is the smallest change in concentration the sensor can conﬁdently discriminate. Viable biosensors require a LOD below the smallest change
in biomarker concentration of clinical relevance. This requirement isstrongly dependent on application, as the physiological concentrationof biomarkers can vary by several orders of magnitude between analytes. For example, in whole-blood metabolites such as glucose arepresent in millimolar concentrations (Psychogios et al., 2011), whereas
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K. Scholten, E. Meng

Table 1
Typical or representative range of glucose and L-lactate in various physiological ﬂuids inhealthy adults.
MediumConcentration (mM)
Glucose

L-Lactate

Blood
Tears
Sweat (heat induced)
Saliva4–7
0.1–0.6
0.1–4
0.01–0.2

0.5–2.2
2–5
5–20
0.1–2.5

Cerebrospinal ﬂuid

2.7–4.4

0.5–2.6

Table 2
List of commonly tested interferents for amperometric enzyme-based sensors (FDA, 2006,
2010, 2013a).
Potential interferent

References

Acetaminophen
Ascorbic acid
Bilirubin
Cholesterol
Creatinine
Dopamine
Ephedrine
L-dopaMethyl dopa
Gentisic acid
Glutathione
Ibuprofen
Tetracycline
Tolbutamide
Triglycerides
Urea
Uric acidBeaumont Laboratory (2017)
Berman (2013)
Al-Tamer and Hadi (1994)
Campbell (1965)
Guilbault et al. (1995)
ARUP Laboratories (2017)

cytokines such as interleukin-6 are in pg/mL concentrations(Fernandez-Real et al., 2001). The requisite sensitivity is also dependent on medium. Table 1 presents concentrations of L-lactate and glucose, common biosensor targets, in several physiological ﬂuids; notethat concentration of both analytes varies discordantly by several orders of magnitude among various media. For example, a biosensor
targeting L-lactate in sweat requires a much lower sensitivity than one
targeting blood or saliva, whereas the sensitivity required to detect
glucose in blood is lower than required for other media.
In the broader ﬁeld of ex vivo devices, there are several examples of
sensors with single molecule detection limits, but in vivo devices oﬀer
dramatically lower sensitivities owing to the challenges of translatingsuch technologies to the physiological environment. Sensitive transducers may be overloaded by physical and chemical noise unlessproperly shielded from interference, and sensitive biological components can suﬀer instability and degradation under in vivo temperatureand pH. The incorporation of nano-structures (e.g. nanotubes, nanoparticles) to increase eﬀective surface area of a biosensor is a common
approach to enhance sensitivity among ex vivo devices (Vaddiraju et al.,2010), however, there are few implantable devices using these materials owing to concerns of biocompatibility and toxicity.
The challenge then is not the design of a high resolution sensor, butmaintaining high sensitivity while addressing simultaneous concerns of
biocompatibility, lifetime, and accuracy. Researchers should be awareof these tradeoﬀs when designing new biosensors; for example, semipermeable membranes, commonly used to block interferents among invivo devices (see Section 3.2), can limit diﬀusion of the analyte anddecrease sensitivity. With these considerations in mind, the broad lessons from ex vivo design are still applicable: utilizing sensitive transducers, maximizing the surface area of the sensing site, increasing thecoupling between biological component and transducer, and minimizing noise are all viable approaches for optimizing sensitivity.

component is rarely a serious concern, but for the amperometric orvoltammetric transducer there is a common problem with interferenceby other electroactive species. Many enzymatic biosensors, including
those for detection of glucose, L-lactate, and L-glutamate, rely on theelectrochemical detection of H2O2 produced from a biocatalytic reaction, and the high potential required to oxidize peroxide can oxidize
interferents including ascorbic acid, L-cystine, uric acid and acetaminophen (the active ingredient in Tylenol), generating a false response (Barr, 2013). A complete list of commonly tested interferents ispresented in Table 2. A large body of work has focused on minimizing
interference from these species, with the most common approach entailing use of a permselective membrane or other semipermeable barrier to block interferents from reaching the electrode. Common examples of membrane materials include Naﬁon®, polyurethane, andcellulose acetate, and a focused review of this topic is provided by
Kulkarni and Slaughter (2016). Though eﬀective at improving selectivity, the use of these membranes can limit sensitivity, increase thetime lag between physiological change and sensor response, and exacerbate biofouling. Other approaches involve reducing the electrochemical potential by either including catalase at the electrode site, or
replacing peroxide with a mediator which oxidizes at a lower potential(e.g. ferrocene). These designs, however, have limited use for implantable biosensors owing to concerns over mediator leaching andtoxicity (Chen et al., 2013; Wang, 2008). Some recent designs use a coimmobilized mediator to directly wire enzymes to the electrode,
avoiding the issue entirely. TheraSense Inc. (Alameda, CA), now Abbott
Diabetes Care, has built a wired-type enzymatic sensor incorporating an
osmium-based mediator (Feldman et al., 2003) which forms the basis of
their commercial glucose monitor, with demonstrated in vivo eﬃcacy.
Selectivity remains an obstacle for the current generation of commercially available biosensor implants despite recent improvements.
Among glucose sensors in particular, erroneous measurements due toacetaminophen continue to be observed in clinical trials (Basu et al.,2016; Maahs et al., 2015), and users of the MiniMed® 670G, a semiclosed loop system for glucose monitoring and insulin delivery, arewarned about interference from xylose, acetaminophen, ascorbic acid,bilirubin, and uric acid (Medtronic, 2017).

3.2. Selectivity
Sensors intended for closed-loop drug delivery require exceptional
selectivity against interfering species. More so than ex vivo devices,
implanted sensors are exposed to a large number of possible interferents
at a wide range of concentrations including a diverse array of exogenous substances present in medications. The risks of anomalous
readings are severe, and represent the greatest public and regulatory
fears regarding closed-loop systems. An automated drug delivery pump,
responding to a false-positive from a confounded sensor, could deliver aharmful or even fatal bolus.
The need for highly selective detection of biomarkers motivates theuse of the biosensors over physiological sensors or non-biological chemical sensors. The interactions between analytes and the biologicalcomponent of biosensors is highly speciﬁc, and if necessary, selectivity
can be tuned through choice of speciﬁc biological component. For example, glucose 2-oxidase is known to react with xylose and other carbohydrates, and so biosensors favor the more speciﬁc glucose 1-oxidase(Wilson and Turner, 1992). In general, the selectivity of the biological3.3. Lifetime
Ideally an implanted biosensor would function indeﬁnitely, providing accurate and reliable measurements for closed-loop drug delivery. In practice, biosensors have ﬁnite, and typically short, operating
lifetime due to one of numerous mechanical, electrical, and/or biological failure modes (Fraser, 1997). Implanted biosensors have to322

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K. Scholten, E. Meng

Fig. 3. Timeline of the events following implantation of a generic glucose biosensor (log-time scale). Estimates are drawn from the following references: (Anderson et al., 2008;
Cunningham and Stenken, 2009; Gough and Bremer, 2000; Joung, 2013; Tse and Gough, 1987; Wang et al., 2015).

for closed-loop applications, therefore requires suppression or mitigation of the FBR. While there is no known method to permanently subvert the response, choice of material has a signiﬁcant eﬀect on the
degree of the immune response. Though we caution against describing
any material as universally biocompatible, certain materials including
titanium (Hille, 1966; Laing, 1973; Steinemann, 1998), platinum(Agnew et al., 1986; Cowley and Woodward, 2011; Stensaas andStensaas, 1978), certain glasses and ceramics (Clarke, 1992; Dokmeci
et al., 1997; Hascup et al., 2013; Wilson et al., 1981; Wolfe and Boyde,
1992), and some speciﬁc formulations of polydimethylsiloxane (Colas
and Curtis, 2004; Kalousdian et al., 1998; Quinn and Courtney, 1988),
polyimide (Haggerty and Lusted, 1989; Lago et al., 2007; Richardson
et al., 1993), Parylene (Hara et al., 2016; Loeb et al., 1977; Schmidt
et al., 1988), and polymethylmethacrylate (King, 2013) have been usedin vivo for extended periods of time with minimal response from thebody (Scholten and Meng, 2015). A landmark study by Gough et al.
(2010) demonstrates a rare example of a biosensor functioning for morethan a year in vivo in porcine trials, and up to 180 days in human trials(Lucisano et al., 2016), with no ﬁbrotic encapsulation, a feat the authors attribute to the use of alumina, titanium, and medical grade silicone in the sensor packaging, as well as a the inclusion of peroxide
catalase in their O2 tension sensor design (see Section 3.3.2). For biosensors requiring a semipermeable exterior to allow analytes to reachthe biological component, many incorporate hydrogels owing to theirhigh porosity, biomimetic mechanical properties, and low rates ofprotein adhesion (Barone and Strano, 2006; Cummins et al., 2013;
Sassolas et al., 2012). PHEMA (poly(hydroxyethyl methacrylate)) andPEG (poly(ethylene glycol))-based hydrogels have garnered particularattention, following several in vivo studies reporting reduced levels ofﬁbrosis, and/or prolonged function of coated biosensors (Abraham
et al., 2005; Quinn et al., 1997; Quinn et al., 1995; Yu et al., 2008). One
notable example is the Senseonics Eversense® (Senseonics Holdings,
Inc., Germantown, MD) continuous glucose monitor, an implantableglucose sensor based on a ﬂuorophore-linked PHEMA hydrogel coating,reported to function for up to 3 months in human trials (Colvin and
Jiang, 2013; Dehennis et al., 2015).
We also caution that that physical design parameters, including size,
shape, and mechanical properties, play a role in mitigating, or exacerbating, the FBR. Results of biocompatibility testing across a broad
spectrum of materials show that hard, sharp, and rough surfaces contribute to more severe inﬂammatory response, greater accumulation ofmacrophages at the sensor interface, and faster formation of thicker
ﬁbrotic capsules (Matlaga et al., 1976; Salthouse, 1984; Veiseh et al.,2015). A growing body of evidence indicates that the mechanicalcompliance of an implant plays a role in triggering FBR; studies ofbiosensor micromotion indicate that the stress of a rigid implantmoving against soft tissue can induce damage and trigger additional
inﬂammation (Helton et al., 2011; Hilborn and Bjursten, 2007). These

contend with the additional challenges of in vivo conditions; the warm,
saline environment can accelerate degradation of electrical components(Bowman and Meindl, 1986) and the physiological response of thehost’s immune system can impede or even destroy sensor function(Gilligan et al., 1994; Moatti-Sirat et al., 1992; Moussy and Harrison,
1994; Pickup et al., 1993; Rebrin et al., 1992; Thomé-Duret et al., 1996;
Wisniewski and Reichert, 2000).
Fig. 3 depicts a hypothetical timeline for the life of a generic implanted sensor. Moisture intrusion of porous materials and the initialphysiological response begin almost immediately. By the end of the ﬁrst
day, the body’s acute inﬂammatory response is underway, and common
biological recognition components suﬀer initial degradation. Over thecourse of several days, as part of a chronic inﬂammatory response, theimmune system attempts to clear the foreign body. Cross-linked enzymes used for detection begin to degrade, and a ﬁbrotic capsule forms
around the implant. By the end of the ﬁrst month, thin barrier layers,
including plastics and glass, are subject to serious water permeation,
threatening corrosion of metal components and electronics. The majority of devices presented in the available literature have eﬀective in
vivo lifetimes of a few days to a month. Of the commercially available
examples, all of which are glucose biosensors, the recommended use is
between 6 and 14 days depending on the model. Only a handful ofreports describe successful multi-month trials in humans with a functioning biosensor (Dehennis et al., 2015; Gilligan et al., 2004; Lucisano
et al., 2016).
The major obstacles to extended biosensor lifetime are (i) the foreign body response, (ii) degradation and consumption of the sensor’s
biological component and (iii) electrical and mechanical failure. Here
we expand upon these challenges in greater detail.

3.3.1. Foreign body response and encapsulation
The foreign body response (FBR) describes the myriad biologicalreactions to the implantation and subsequent presence of a foreign
entity. The typical sequence consists of an inﬂammatory response and asubsequent wound-healing response. Immediately following insertion,
damaged tissue and vasculature release platelets and coagulants which
agglomerate at the wound site, followed by protein adsorption onto theexterior of the biosensor. The acute inﬂammatory response is characterized by the inﬁltration of mast cells and neutrophils at the implantsite, followed by a chronic inﬂammatory response wherein macrophages and foreign-body giant cells attempt to clear the device. At
between 1 and 4 weeks, a ﬁbrotic collagen capsule forms around theimplant, typically rendering the sensor nonfunctional. For a thoroughdescription of the underlying biomolecular processes comprising FBR,
we refer the reader to several excellent reviews which focus on the topic
in detail (Anderson et al., 2008; Cunningham and Stenken, 2009; Soto
et al., 2016; Wang et al., 2015).
Long term, reliable function of implantable biosensors, as required323

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K. Scholten, E. Meng

results have great consequence for the common percutaneous ‘needletype’ design that dominates the ﬁeld of implantable, electrochemicalsensors. The choice of tethering the sensor to the skin may result ingreater FBR, and functionally shorter lifetime, compared to fully implanted ‘free-moving’ sensors. Contrary to what one might assume,
there is no strong dependence of implant size on the magnitude of FBR,
at least for macroscale implants (major dimensions > 100 µm), though
there may be a dependence on magnitude of insertion trauma (Veiseh
et al., 2015; Wang et al., 2015). For microscale devices (major dimension < 100 µm), size appears to play a signiﬁcant role. Sanders
et al. (2000) describe the FBR to microscale polymer ﬁbers decreasing
with size, and abating entirely for structures < 6 µm diameter. Thisﬁnding is in agreement with recent reports of ﬂexible neural probeswith sub-cellular dimension (< 15 µm width) exhibiting excellent
chronic in vivo performance and encountering minimal scar tissue encapsulation (Luan et al., 2017; Patel et al., 2015; Schwerdt et al., 2017).
With improvements in sensor miniaturization, soft, ﬂexible, sub-cellular
biosensors could be fabricated which avoid the FBR entirely, enabling
the long indwelling sensors required for chronic closed-loop applications.
Other strategies to extend sensor lifetime include use of drugs to
disrupt or mitigate FBR. This approach is commonly used for intravascular sensors, as device presence can trigger blood coagulationand thrombosis. For such devices, thin-coatings of anti-coagulants suchas heparin, and NO releasing polymer coatings have been used successfully in vivo (Nagaoka et al., 1990; Wu et al., 2007; Yang et al.,1997). For subcutaneous and percutaneous biosensors, several in vivo
tests have shown mitigated inﬂammation, thinner ﬁbrotic encapsulation, and/or improved sensor lifetime through use of dexamethasone,(Klueh et al., 2007; Kozai et al., 2016; Vallejo-Heligon et al., 2016) thetyrosine-kinase inhibitor mastinib (Avula et al., 2016; Avula et al.,2013), and NO releasing coatings (Soto et al., 2014). The eﬀects can beextended up to several weeks by incorporating drugs into slow dissolving polymer microspheres for time-delayed release (Gu et al., 2015;
Hickey et al., 2002; Zolnik and Burgess, 2008), and recent work has
explored the use of multidrug compartments for dosing strategies incorporating staggered release of multiple drugs (Morris et al., 2017).
Dexamethasone loaded coatings in particular have emerged as a signiﬁcant breakthrough in FBR mitigation for implantable biosensors.
Glucose sensors with dexamethasone coatings have been tested underchronic in vivo conditions, showing signiﬁcant improvements in sensor
lifetime and sensitivity up to 21 days (Vallejo-Heligon et al., 2016;
Vallejo-Heligon et al., 2014). More recently dexamethasone coatings
have been incorporated into clinical studies for commercial eﬀorts; the
PRECISE study examined the Senseonics Eversense® glucose sensor over
180 days, and featured a silicone dexamethasone acetate collar to reduce chronic tissue inﬂammation (Kropﬀ et al., 2017). Soto et al.
(2016) provide a more thorough examination of the use of anti-inﬂammatory compounds for sensor biocompatibility.
An alternate approach entails the use of angiogenic growth factors
to promote the formation of vascular tissue around an implant, tailoring
the FBR as opposed to attempted mitigation (Ward, 2008). Vascularendothelial growth factor (VEGF) has been demonstrated to promote
neovascularization during the encapsulation of an implant, permitting
diﬀusion of analytes across the capsule, resulting in increased biosensor
performance and lifetime (Klueh et al., 2005; Ward et al., 2003; Ward
et al., 2004). VEGF can also be combined with dexamethasone in slowrelease polymer-coatings to both minimize inﬂammation and promote
vascular tissue simultaneously (Kastellorizios et al., 2015; Patil et al.,2007).

transient nature of biological materials drives chronic sensor drift, andultimately limits the functional lifetime of implanted biosensors.
Bioaﬃnity sensors, those relying on biomolecular speciﬁc binding
to antibodies, aptamers, or other receptor sites, are fundamentallyconsumptive by design. The bindings are speciﬁc yet irreversible; asavailable receptor sites are depleted, the sensor is gradually used up.
This is the primary reason why aﬃnity sensors are so rarely used in for
implantable applications, despite being so commonly used for ex vivo
devices. Implantable aﬃnity sensors require weak, reversible bindings
to analytes, which limits sensitivity and selectivity, or a means to periodically strip bound analytes. Such designs are uncommon, and typically aﬃnity sensors are simply not pursued for chronic in vivo applications.
Enzymatic biosensors, where the biological component serves as acatalyst and not a receptor, are in principal non-consumptive, althoughsome require a mediator which must be present in the environment at
suﬃcient concentration (e.g. dissolved O2 for typical glucose oxidase
reaction), which can be depleted. These biosensors are still subject tothe gradual deterioration of the enzyme protein. While many commercial enzymes are stable for months when stored under refrigeration
or at room temperature for single-use ex vivo biosensors, under conditions of physiological temperature and pH they can denature, undergo
conformational changes, or oxidize. For example, the glucose dehydrogenase enzyme GLD-PQQ, which is used in several blood glucose
tests, has an in vivo lifetime of only a few hours to a few days (Okuda
et al., 2004). Even ‘stable’ enzymes require chemical crosslinking andcareful sensor design when intended for prolonged in vivo use. The
soluble form of the glucose oxidase enzyme will persist in an in vivo
environment for only a few days (Gough and Bremer, 2000; Valdes andMoussy, 2000), but exhibits a half-life of approximately 3 months following cross-linking with glutaraldehyde (Tse and Gough, 1987).
During sensor operation, however, the enzyme tends to degrade much
more quickly due to the action of H2O2 generated by the catalyzed
reaction with glucose (Gough and Bremer, 2000; Harris et al., 2013). In
practice, crosslinked glucose oxidase has an operational half-life of afew days to 1 week (Towe et al., 1996; Valdes and Moussy, 2000).
Implantable biosensors typically circumvent this limitation by incorporating an excess of glucose oxidase and recalibrating multiple
times a day to account for sensitivity drift. The O2 tension-sensor design
employed by Lucisano et al. (2016) incorporates an excess of immobilized catalase, to mitigate peroxide-induced oxidase inactivation.
In this design, paired electrochemical oxygen sensors measure theconsumption of ambient O2 during the catalyzed oxidation of glucose,and immobilized catalase decomposes the produced peroxide. The authors attribute this design feature, in part, to the long-lifetime of theirimplanted devices. More recently, researchers have explored novel
methods to immobilize enzymes as a means to increase stability, including incorporating enzymes into silica and titania based sol-gels, andbinding enzymes to nanostructures such as carbon nanotubes and metal
nanoparticles. To date, however, no strategy has yielded signiﬁcantly
improved in vivo stability. Reviews by Sassolas et al. (2012) and Harris
et al. (2013) explore progress on these methods in greater detail.
3.3.3. Mechanical and electrical failure
Common mechanical failures are driven by chronic fatigue andstress: broken wires, worn components, and cracked casings. Common
electrical failures are driven by gradual moisture intrusion: electricalshorts, corrosion, and component failure from ionic contamination.
Relatively little discussion is awarded to these problems in much of theliterature, as they are rarely concerns in acute proof-of-concept testing.
Instead these issues manifest in chronic use (multi-day to multi-month),
due to cumulative damage of natural tissue micro-motion, and prolonged exposure to the warm saline environment of the body.
Implantable sensors should be physically robust, with the expectation that devices will face continual, if low magnitude, strain and motion. Alternatively, devices can be made intentionally ﬂexible, an

3.3.2. Degradation and/or consumption of biological components
The biological component of a biosensor has a limited, and typicallyshort, lifetime under in vivo conditions. Biological receptors and enzymes can degrade gradually, fail catastrophically, or be consumed
either by the host’s immune response or under sensor operation. The
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is calculated with Eq. (3), for a sample of N measurements, where
fsensor(ti) is the response of the sensor at timepoint ti, and fref(ti) is thecorresponding reference value.
N

MARD =

1∑
N i

fsensor (ti )−fref (ti )fref (ti )(3)MARD values represent the variation in measurement between asensor and a reference as averaged across a sample population, withlow values suggesting an accurate sensor. High MARD values may be
indicative of low selectivity, high noise, persistent bias, or other confounding factors, though the value itself may not convey how accurately a sensor tracks rates of change in analyte levels, or capture thelikelihood of signiﬁcant error. Commercial CGMs have reported MARD
values ranging from just below 10% (FDA, 2016a) to over 25% (FDA,2007), which are high compared to established self-monitoring blood
glucose (SMBG) systems capable of MARD values below 5%(Freckmann et al., 2015).
Major sources of inaccuracy include transient decreases in sensitivity and sensor drift induced by FBR encapsulation and biofouling (seeSection 3.3.1), presence of interferents (see Section 3.2), and the timelag between changes in blood glucose levels and corresponding changes
in interstitial ﬂuid (Kovatchev et al., 2009; Rebrin et al., 1999; Steilet al., 2005; Wentholt et al., 2007). The latter is a major consequence of
subcutaneous placement of CGM devices, and can be exacerbated by theoften complicated diﬀusion dynamics between blood, interstitial ﬂuid,
and biosensor, which can confound sensor calibration and measurement (Cengiz and Tamborlane, 2009). Eﬀorts to improve CGM accuracy
is a major focus of continuing research; below we describe several
avenues to improve accuracy through advances in biosensor design, but
we note considerable eﬀorts also focus on changes to the algorithms
and software which ﬁlter and interpret CGM measurements(Facchinetti, 2016; Facchinetti et al., 2013; Rebrin et al., 2010).
CGMs may supplement (adjunctive use) or replace (non-adjunctiveuse) conventional SMBG measurements such as ﬁnger-stick tests. In
typical use, CGMs report glucose measurements every few minutes toan external controller, which users can then forward to an insulin infusion pump. The majority of commercial devices to date have been
approved only for adjunctive use, meaning treatment decisions must be
accompanied by SMBG conﬁrmation, and the few non-adjunctive devices available still require daily calibrations using the more reliable
SMBG measurements. In recent years, trials of closed-loop drug delivery(insulin infusion) have been conducted using CGMs as the biosensor
input, and commercial development of the ﬁrst closed-loop systems iscurrently underway. For a comprehensive discussion of CGM biosensors, their design, operation and use, and their role in type 1 diabetes management, we refer the reader to the excellent text by(Cunningham and Stenken, 2009). In addition, several recent articles
provide complementary reviews of critical topics: Wang and Lee (2015)
provides a thorough description of the chemical mechanisms underlying several glucose biosensors, Bruen et al. (2017) reviews non-subcutaneous glucose biosensors, which are not emphasized here, andChen et al. (2017) provides a detailed look at the state of the art in CGM
technology.

Fig. 4. Approximate material dependent permeability of water vapor as a function ofmaterial thickness. Reprinted from Joung (2013).

approach driving recent research into soft polymer-based biomedical
devices (Liao et al., 2015; Scholten and Meng, 2015). Wires, particularly those crossing the skin, are a potential weak-point, as natural
micro-motion may induce fatigue failure. Electrical insulation should beselected for chronic exposure to physiological ﬂuid at elevated (37 °C)
temperatures. Polymers commonly used in the electronics and medical
industries and typically considered water resistant (e.g. silicone,rubber, most organic polymers) can fail within a few days under these
conditions. Even thin ﬁlms of materials typically renowned for theirbarrier properties (e.g. Parylene C) can suﬀer moisture intrusion fromprolonged in vivo exposure. Fig. 4 depicts approximate time-to-failure
for varied classes of materials at diﬀerent thicknesses. Note that evenrelatively thick (mm) layers of common insulating materials (glass,polymers) are subject to signiﬁcant moisture intrusion within a few
days to weeks. While implantable sensors intended for acute application
may survive with thin coatings of high-density organic or ﬂuoro-carbon
polymers, long-indwelling sensors may require hermetically sealed
packaging of glass or metal.
4. Applications for closed-loop drug delivery
There are only a few fully-realized examples of closed-loop drugdelivery systems, and only a few biosensor technologies mature enoughto provide the continuous real time measurements required to develop
such systems. The majority of current research has focused on the development of implantable glucose sensors for closed-loop control of
insulin and blood sugar levels, while other applications remain in theearly development stage or are entirely theoretical. Here we will describe the current state of implantable biosensors as used in, or withapplication for, chronic closed-loop drug delivery.
4.1. Continuous glucose monitoring
Glucose biosensors make up the overwhelming majority of biosensors in production, and continuous glucose monitors (CGM) make up
the majority of implantable biosensors currently under study. CGMs areused to detect changes in blood glucose levels for guiding treatment(including insulin or glycogen infusion) for type 1 diabetes. Typical
blood glucose levels range from 4 to 7 mM, with hypoglycemic symptoms appearing at levels below 4 mM and hyperglycemic symptoms
appearing above 11 mM. Sub-millimolar resolution is desired for CGM
applications, though this requirement rarely poses a major obstacle.
Sensor accuracy remains the largest challenge facing CGM development, with reported values from implanted devices varying substantially from laboratory standards. Sensor accuracy is typicallyquantiﬁed as the mean absolute relative diﬀerence (MARD) between
CGM measurement and blood glucose reference measurements. MARD

4.1.1. Electrochemical glucose biosensors
The majority of glucose biosensors are enzymatic. The basic design
consists of an immobilized ﬁlm of glucose oxidase enzyme, serving as
the biological recognition layer, and a set of electrodes (typically a Pt
working electrode and Ag/AgCl reference), serving as the transducer.
These devices have been reviewed extensively, and for a more thoroughdiscussion of electrochemical glucose biosensors we direct the reader toreviews by Wilson and Giﬀord (2005), Newman and Setford (2006),
Wang (2008), and Vashist (2013). There are four basic architecturescommonly represented in the literature, all of which involve amperometric detection of the co-products consumed or generated during the325

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Fig. 5. Diagram of the four common designs for amperometric, enzyme-based biosensors: (a) First generation sensor measuring oxygen-tension, (b) second generation sensor measuringhydrogen peroxide, (c) second generation sensor with artiﬁcial mediator, (d) third generation sensor with directly wired enzyme.

strategies to mitigate this problem, including use of a permselective
diﬀusion membranes to block interferents, can aﬀect sensitivity andsensor lag by slowing diﬀusion of glucose to the sensor (see Section
3.2). Additionally, the action of H2O2 on the glucose oxidase enzyme
greatly accelerates enzyme degradation, limiting overall lifetime(Gough and Bremer, 2000; Harris et al., 2013). These issues motivatedthe development of second and third generation glucose sensors.
Fig. 5c depicts a second generation sensor. The design is functionally similar to a ﬁrst generation peroxide sensor, with the additional
component of mediator compound co-immobilized with the enzyme
layer. The mediator serves to replace the O2/H2O2 redox pair; during
typical operation the mediator is reduced as a consequence of the enzymatic oxidation of glucose, and then oxidized electrochemically by
the anode. Popular examples of mediators include ferricyanide andferrocene derivatives (Cass et al., 1984). Second generation sensors canoperate at lower potentials, reducing risk of interference from electrooxidizable compounds, and are not dependent on physiological O2
concentration. While use of mediators is common in the design of exvivo sensors, second generation glucose sensors have seem limited deployment for in vivo applications due to concerns of mediator stability
and possible toxicity (Wang, 2008). Third generation sensors (Fig. 5d)directly “wire” the enzyme to the electrode, replacing the need for any
intermediary, and allow electrons to transfer directly from the redoxcenter of an enzyme to the electrode (Bott, 2004; Feldman et al., 2003).
These sensors largely ameliorate issues of mediator solubility, electroactive interference, and O2 depletion. Though this approach is considerably less explored than the others, in part due to the diﬃculties
associated with creating an eﬃcient and biocompatible wiring element,
an osmium-polymer based wired enzyme device has been successfullycommercialized through the Abbott Freestyle® Navigator andoxidation of glucose to gluconic acid. Diagrams depicting the operationof each design are presented in Fig. 5.
First generation sensors rely on physiological O2 as the electron
acceptor, consuming O2 during the catalyzed oxidation of glucose andproducing H2O2. Fig. 5a depicts a ﬁrst generation O2-tension sensor; thedesign consists of two working cathodes enclosed in a semi-permeable
membrane, with a one functionalized with immobilized glucose oxidase
and a bare cathode serving as a control. Both working electrodes areheld at a potential diﬀerence (∼0.6 V) compared to a combined reference/counter anode. Ambient O2 is electrochemically detected atboth cathodes, and the measured signal is the diﬀerence in current
between the electrodes. As the glucose oxidase reaction consumes
ambient O2, the diﬀerence in the ﬂux of O2 to the electrode surface is
proportional to the rate of glucose oxidation.
Fig. 5b depicts a ﬁrst generation peroxide sensor. These devices
consist of a single working anode, coated with immobilized glucose
oxidase, and a combined counter/reference electrode. Physiologicalglucose is oxidized at the enzyme to gluconic acid, resulting in the
consumption of O2 and production of H2O2. H2O2 is detected electrochemically at the anode surface, and the resulting current is measured
as a signal of glucose concentration. This design is the most commonlyused in research and industry, owing to its simplicity and extensive
development history, and biosensors mimicking this approach withother enzymatically oxidizable biomarkers are ubiquitous. Thoughcommon, ﬁrst generation sensors suﬀer from several inherent limitations, notably a reliance on local O2, which can be consumed during
operation or limited due to complications from the foreign body response (i.e. scar tissue encapsulation). The high over-potential required
for the oxidation of H2O2 (approximately 0.7 mV) also promotes interference from other electroactive species (Barr, 2013). Common
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Freestyle® Libre lines of implantable glucose monitors (Distiller et al.,2016; Heller and Feldman, 2010).
The sensitivity of electrochemical CGMs can vary widely, but typically span the range of 1–10nA/mM, with detection limits on the orderof 0.1–1 mM. Sensor lifetime is typically limited by a combination offoreign body response and glucose oxidase instability to between a few
days and a few weeks. The failure of devices is typically gradual. Due todiminishing quantities of active enzyme and increasing encapsulation
by ﬁbrotic tissue, the sensitivity of implanted devices decreases steadily
over the course of its lifetime, requiring frequent (twice a day) calibrations using reference SMBG measurements.
The most common sensor geometry is a percutaneous needle, suchthat the electrodes can be inserted shallowly under the skin for measurement of interstitial glucose from subcutaneous tissue. This design
avoids the complications associated with intravenous implantation,
minimizes invasiveness, and allows users to easily implant the sensors
at home. The success of the implantable glucose biosensor can in partbe attributed to the versatility of this design, which is made possible by
the strong correlation between interstitial and blood glucose values(Kulcu et al., 2003; Rebrin and Steil, 2000). The percutaneous design
also allows for ancillary electronics to be attached to the outside of the
body. A common packaging solution has the sensor connect to an external wireless transmitter, which can then transmit data to an external
controller or smartphone that can display sensor readings or direct aninfusion pump for closed-loop applications.

Fig. 6. Diagram of an implantable glucose sensor relying on a ﬂuorescent glucose-sensitive hydrogel.

presence of physiological glucose, and the resulting emission is thenmeasured by either an internal or external photodetector. Tokuda et al.
have presented preliminary in vitro and in vivo results using such adesign (Tokuda et al., 2016; Tokuda et al., 2014); successful detection
and long sensor lifetimes are reported, but with sensor responses lagging signiﬁcantly (> 10 min) behind blood glucose measurements. A
similar device built under a commercial eﬀort by Senseonics incorporates both a light source and detector (DeHennis et al., 2016;
Dehennis et al., 2015). The Senseonics Eversense® comprises a LED, aphotodiode, CMOS controller, and wireless antenna in a polymer capsule surrounded by a glucose-sensitive hydrogel. The ﬂuorescent signal
is both excited and recorded at the site of the implant, and a wirelesssignal is transmitted conveying the glucose concentration. The Eversense® has been tested in humans for up to 3 months, with reported
MARD value of 11.1%, comparable to presently available continuous
glucose monitors. The device is currently under application for regulatory approval for sale, and will likely be the ﬁrst ﬂuorescence-based
device to market. The impressive sensor lifetime represents a signiﬁcantadvantage over current electrochemical designs, and the use of wirelesselectronics should enable easy integration with existing insulin infusion
pumps for future closed-loop control. For a more focused review ofboronic-acid based glucose sensors, including non-ﬂuorescent designs,
we direct the reader to recent work by Lacina et al. (2014).

4.1.2. Fluorescent glucose biosensors
Fluorescent glucose biosensors were developed in parallel withelectrochemical sensors, though they have yet to achieve the same levelof clinical success or widespread use. The concept is simple and enticing: a substrate is loaded with a glucose-sensitive ﬂuorophore, implanted subcutaneously, and the ﬂuorescent signal is measured acrossthe skin with an external device. In principle, a ﬂuorescent sensor could
function continuously for long periods of time without need for percutaneous wire connections, implanted batteries, or wireless telemetry.
The majority of reports on such devices describe variations on the
bioaﬃnity design ﬁrst developed by Schultz and Sims (1979). In thisapproach, the protein concanavalin A serves as a bioaﬃnity receptor for
glucose, glucose binds to concanavalin A, displacing a ﬂuorescently
tagged competing sugar (e.g. dextrose), which in turn modulates thetotal ﬂuorescent signal of the assembly. An alternative approach by
Barone et al. relies on catalysis by the glucose oxidase enzyme, and thenear-infrared ﬂuorescence of single-walled carbon nanotubes. Similar
to electrochemical methods, glucose is oxidized by the enzyme, and theco-product H2O2 modulates the ﬂuorescence, either by acting on thesurface chemistry of the nanotubes (Barone et al., 2005) or changing
the density of nanotube agglomerations (Barone and Strano, 2006;
Barone et al., 2009). Despite considerable study, neither approach has
led to a practical sensor for continuous glucose monitoring, let alone
application to closed-loop systems. Major obstacles include risk of
toxicity from concanavalin A (Ballerstadt et al., 2006) and/or nanostructures, limited lifetime due to ﬂuorophore photo-bleaching, anddiﬃculties achieving reliable responses.
In contrast, researchers exploring boronic-acid derived ﬂuorescenthydrogels have made rapid strides towards a fully-functioning device.
Boronic/diboronic acids are a well-established class of glucose-sensitive
ﬂuorophores, with demonstrated reversibility and high-sensitivity(Fang et al., 2004; Kawanishi et al., 2004). Hydrogel composites containing a boronic acid derived monomer, which ﬂuoresces under nearUV light with exposure to glucose, have been successfully tested in vivo
with microsphere and microﬁber form-factors (Heo et al., 2011; Heoand Takeuchi, 2013; Shibata et al., 2010). In recent years, researchers
have developed electronic implants built around this technology. Fig. 6
shows a diagrammatic example of such a device, which entails awirelessly powered UV LED light source, with a glucose hydrogel
coating. LED excitation stimulates the boronic-acid ﬂuorescence in the

4.1.3. Clinical trials demonstrating improved outcome with closed-loop
controlOver the past decade, a series of clinical trials demonstrated thesafety and eﬃcacy of closed-loop insulin delivery for patients with type1 diabetes relying on CGM measurements. These trials have grown insize and sophistication, reﬂecting reﬁnements in technology andmethodology and greater conﬁdence in the safety of the closed-loop
approach. The ﬁrst published studies tested over-night control ofclosed-loop devices, relying on glucose measurements from commercially available subcutaneous glucose monitors, and laptop controllers
to direct insulin infusion (Bruttomesso et al., 2009; Clarke et al., 2009;
Hovorka et al., 2011; Steil et al., 2006). Investigators reported favorable
performance of the closed-loop systems: comparable or lower rates ofhypoglycemic events when compared to open-loop protocols, higher
percentage of time spent within targeted blood glucose levels, andcritically, few-to-no adverse events arising from the reliance on theclosed-loop systems. These trials, however, excluded exercise or meals
which pose the greatest challenge to glucose control.
The advent of smartphone software capable of managing the algorithmic control of insulin infusion enabled out-patient/at-home clinical327

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trials of increasingly longer duration (Kovatchev et al., 2014;
Leelarathna et al., 2014; Thabit et al., 2014). Bergenstal et al. (2016)
reported on the largest such trial to date, involving 124 out-patientparticipants (no control group) over a 3 month period. Participantsrelied on the closed-loop system night and day, providing daily calibrations and carbohydrate estimates for meal boluses, but otherwise
allowed the system to regulate insulin doses automatically in responseto an implanted CGM measurements. The authors reported no signiﬁcant number of adverse incidents over the period, and a percenttime in recommended blood glucose range of 72.2% by study end,
comparable to other closed-loop studies. A series of recent crossoverstudies have compared closed-loop systems to sensor-augmented-pumptherapy, wherein patients rely on data from a CGM for self-administered insulin treatment (Kropﬀ et al., 2015; Renard et al., 2016;
Tauschmann et al., 2016; Thabit et al., 2015). The results indicate aslight advantage in closed-loop control over manual control, both for
decreasing rates of hypoglycemic events and in increasing time spent inrecommended blood glucose range. This outcome suggests the closedloop approach oﬀers fundamental advantages in maintaining glucose
control, beyond that of patient convenience and adherence.
With several commercial actors currently developing closed-loopsystems for market, there is a need for clinical studies with larger populations and larger study periods. In response, four large clinical trials
are currently recruiting or underway, each of which will involve over
100 participants and lengthy trial periods (National Institutes of Health,2017). Future work would ideally attempt controlled comparisons of
sensor type or system design. In particular, there has been little work
comparing model predictive control algorithms vs. proportional integrative derivative algorithms (Pinsker et al., 2016), and more clinicalresearch might shed important light on eﬀective diﬀerences.

Fig. 7. Performance of selected CGM biosensors compared to blood glucose measurements, measured as mean absolute relative diﬀerence (MARD) between CGM values andthose from comparator methods; Presented values are reported aggregate values from adisparate collection of academic studies, clinical trials for FDA approval, and manufacturer reports, under diﬀerent experimental conditions, and as such cannot be considered a rigorous comparison of sensors or manufacturers. The data is presented here todemonstrate the broad trend of improving sensor accuracy as measured by MARD value.
Dexcom (circles): STS™ (FDA, 2007); SEVEN® (Zisser et al., 2009); SEVEN® Plus (Bailey
et al., 2009); G4® (FDA, 2014); G4® with 505 Update (FDA, 2014); G5® (FDA, 2016a);
Medtronic (squares): Guardian® RT (FDA, 2005); Paradigm Revel™ (FDA, 2015); Enlite®:
530G system (FDA, 2013b); Guardian® 3: 670G system (FDA, 2016b); Abbott Laboratories(diamonds): Freestyle® Navigator (Weinstein et al., 2007); Freestyle® Navigator II(Vashist, 2013); Freestyle® Libre Flash (Bailey et al., 2015); Presented by manufacture inorder of release.

Kovatchev et al. (2015) and Wilinska and Hovorka (2014) suggested
use of sensors with errors greater than 10% could be responsible forincreased rates of hypoglycemia.
Over the past ten years the accuracy of commercial sensors has
continuously improved, a result of reﬁnements in sensor design, systemarchitecture, and software. This trend is visible in Fig. 7, which plots
reported MARD values over four generations of commercial CGM releases. The most recent generation of CGMs have achieved a < 10%
MARD value, clearing the ﬁnal hurdle for non-adjunctive use. The
FreeStyle® Libre Flash, built around an implantable subcutaneous biosensor, was approved in the European Union as a SMBG replacement
under certain conditions in 2014 (Abbott Media Room, 2014), andapproved in the United States for sale as the ﬁrst CGM which does not
require SMBG calibration (FDA News Release, 2017). In 2016, theDexcom G5® became the ﬁrst CGM system to receive non-adjunctive
labeling in both the United States and European Union, though users
are still required to calibrate the device twice a day with ﬁnger-sticks
measurements, and must manually administer insulin doses. Finally,
the FDA approved the MiniMed® 670G for sale in 2017 (FDA, 2016c).
The system provides closed-loop control of basal glucose levels using a
CGM sensor and wearable insulin pump. The device is formally identiﬁed as a hybrid-closed-loop system, as users are still required tomanually deliver meal boluses, input information regarding diet andexercise, and calibrate the sensor to SMBG data 2–4 times per day (FDA,
2016b). These limitations notwithstanding, the MiniMed 670G® is theﬁrst consumer device capable of administering therapy in direct response to sensor readings, and as such, represents a tremendous step
towards a fully closed-loop system.
Partnerships between Dexcom and Tandem Diabetes Care
(Businesswire, 2016), Abbott and Bigfoot Biomedical (Prnewsire,2017), and Senseonics and Roche (Businesswire, 2017b), have all announced eﬀorts to create competing closed-loop systems with releases
expected over the next two to three years. In the interim, several newcommercial actors have declared intentions to release alternative
closed-loop systems and CGM paradigms. Examples include: Inreda,
which has completed early clinical trials with a bi-hormonal closedloop system (Blauw et al., 2016), Verily Life Sciences, which is developing a CGM sensor embedded in a contact lens (Barrettino, 2017), and4.1.4. Towards commercially available closed loop systems
Investment by private industry into implantable biosensors has focused almost solely on the development of CGM devices for the treatment of diabetes mellitus. The ﬁrst commercially available incarnation,
the MiniMed CGMS®, was restricted for physician-use; the device wasinserted under the patient’s skin and retrieved after three days todownload and analyze glucose levels retrospectively. In the years that
followed, the technology and industry matured rapidly. Today threecompanies oﬀer commercially available CGM sensors (Medtronic,
Dexcom, and Abbott), all of which oﬀer real-time monitoring of glucose
levels and, to varying degrees, software integration with insulin pumps,
SMBG devices, and smartphones. Current sensors are considered reliable for 6–7 days, with recent iterations of the FreeStyle® Libre (Abbott
Laboratories) proven reliable for up to 14 days (Hoss et al., 2013).
The industry focus on CGM sensors can be traced to a conﬂuence ofﬁnancial and technical factors. The target market is very large:
30 million type 1 diabetics globally (WHO, 2016) that are familiar withat-home diagnostics and treatment from decades of using SMBG devices
and manual insulin injections. The glucose oxidase amperometricsensor design underlying current commercial technology is simple, effective, and well-established. The ubiquity of SMBG devices provides asimple method for frequent calibration to compensate for sensor drift.
These factors have minimized the technical obstacles in commercial
CGM development, while providing suﬃcient impetus for the substantial investments necessary for regulatory trials.
Until recently, regulatory agencies have strictly limited the role of
CGM sensors in informing treatment decisions (i.e. insulin delivery).
The ﬁrst several generations of commercial CGM devices were labeled
only for adjunctive use, as a complement to SMBG devices (Castle andJacobs, 2016; Edelman, 2017), and were used to discern changing
trends in glucose levels, or to warn of impending hypoglycemia, but not
to make deﬁnite determinations of glucose levels. These restrictions
were predicated on the low accuracy of CGM sensors. The ﬁrst commercial generation could err by as much as 20% in clinical comparisonswith SMBG measurements, as measured by MARD value, and studies by328

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and mouth guards (Kim et al., 2014), respectively. These devices oﬀer
continuous monitoring without the invasiveness of fully-implanted
sensors, though further in vivo studies are required to verify their accuracy and long-term stability.

Nemura Medical, which has announced development of a disposable,skin-mounted CGM (Businesswire, 2017a). There is also growing interest in the development of intraperitoneal CGM sensors, on the basisof improved glucose sensing kinetics (Burnett et al., 2014; Fougner
et al., 2016), and the larger peritoneal space (Zisser et al., 2015).
Huyett et al. (2016) have presented chronic performance of intraperitoneal CGM in animal models, and researchers from Trondheim
University have declared exploration of a double intraperitoneal closedloop system, featuring a linked intraperitoneal CGM and insulin pump
(Stavdahl et al., 2016). The ﬂurry of new attention is a positive forfurther advances in the ﬁeld, though it is unclear how many of themany recently proposed ventures will successfully reach market.

4.2.2. Chemotherapeutics and anti-cancer medication
Chemotherapeutics and other anti-cancer drugs often have narrowwindows of therapeutic concentrations, above which agents become
toxic and below which they are rendered ineﬀective. Blood concentration of chemotherapeutics is considerably lower than metabolites
such as glucose and L-lactate, with typical ranges between single nanomolar and single micromolar values. Optimizing drug concentration
can be diﬃcult due to pharmacodynamic dependence on patient mass,age, sex, body-surface area and genetics, and optimizing dosing regimen is further frustrated by the varying pharmacokinetic factors
modulating the body’s uptake, metabolism, and excretion of the drug.
For decades, oncologists have experimented with pharmacokineticguided (PK-guided) dosing, using direct blood measurements of drugconcentration to individually tailor dosage. Use of PK-guided dosing
has proven eﬀective for several common anti-cancer drugs (e.g. busulfan and ﬂuorouracil) with evidence of improved outcomes and lower
toxicity (Gao et al., 2012; Patel and Papachristos, 2015; Walko andMcLeod, 2008). In principle, this approach can be expanded to create aclosed-loop chemotherapy system. By monitoring in vivo drug concentrations in real-time, a feedback system can continuously adjust therate of drug delivery, ensuring the patient receives a dosage thatminimizes side-eﬀects and toxicity while maximizing therapeutic effectiveness. A closed-loop approach is well suited for the many cancer
treatments which require long sessions of intravenous delivery fromautomated infusion pumps, and may oﬀer improved outcomes forseveral therapies, but to date, little work has been done in this area.
This is in part due to the lack of a generalizable physical or biological response to serve as a proxy for in vivo chemotherapeutic concentration. Drug concentrations must be measured directly, typicallyusing laborious and slow analytical techniques (e.g. immunoassay orHPLC); the development of a closed-loop system ﬁrst requires a biosensor capable of rapid measurements of circulating drug concentrations. Recent advances in electrochemical biosensors provide the most
likely platform. Soh and co-authors recently presented an aptamerbased voltammetric biosensor that enabled the ﬁrst demonstration ofclosed-loop control of doxorubicin in rabbits (Ferguson et al., 2013;
Mage et al., 2017). The sensor is designed for ex vivo operation andrequires catheterization of the patient for continuous blood ﬂow, but
the fundamental design is amenable to miniaturization and implantation with improvements in packaging. An implantable design has been
demonstrated by Baj-Rossi et al. (2012); the device is a voltammetricbiosensor which exploits isoforms of the cytochrome P450 enzyme forelectrochemical detection of several chemotherapeutics. This biosensor
has been incorporated into an implantable, wireless package, withpreliminary work performed to examine the biocompatibility of thedesign (Baj-Rossi et al., 2014); however, the sensor has not yet been
deployed in PK-guided dosing or closed-loop applications. The key innovation of these sensors is the rapid response times and the generalizability of the platform. In principle, these sensors could be adapted for
detection of any small-molecule pharmaceutical for which an appropriate aptamer or enzyme isoform can be found.
Though there are no current examples of implantable biosensors
used in closed-loop control of chemotherapy or other anti-cancer
treatment, recent advances in biosensor development and recent trends
in cancer treatment seem likely to converge on this technology. There isa growing focus within oncology on personalizing therapy, and a
growing emphasis on minimizing toxicity. Successful trials of PKguided dosing will motivate healthcare providers and patients to seek
greater access to the practice, and a biosensor-based, closed-loopsystem eliminates many of the major technical hurdles, particularly thetime and resources required for frequent bloodwork analysis. The

4.2. Non-glucose biosensors: potential and future applications
Despite the recent advances in closed-loop insulin control using
implantable glucose biosensors, there are currently no comparable
systems targeting other analytes. There are, however, several promisingavenues of implantable biosensor research with potential closed-loop
applications. Here we describe several promising state-of-the-art research eﬀorts.
4.2.1. Lactate
L-lactate (lactate) is a key metabolite and a predictive biomarker forconditions such as lactic acidosis, heart failure, hypoxia, hemorrhage,and shock (Kotanen and Guiseppi-Elie, 2013; Rassaei et al., 2014).
Typical blood plasma levels span 0.5–2.2 mM (ARUP Laboratories,2017). Measurements of blood lactate concentration have clinical relevance, particularly for trauma patients; elevated lactate levels areassociated with higher mortality rates (Nguyen et al., 2004), and asudden spike in lactate concentrations may indicate impending circulatory failure (Bakker and De Lima, 2004). Continuous lactate measurement would provide a new strategy for monitoring intensive carepatients. A continuous lactate monitor could trigger an alarm to notify
physicians in the event of suddenly rising concentration, or even trigger
an automated therapeutic intervention in a closed-loop emergency response system. The necessary sensor technology is largely established.
Lactate biosensors are a mature technology in use for point-of-careblood measurements in trauma and sports medicine (Rathee et al.,2016). The most common design is an enzyme-based amperometricsensor, comprising a set of electrodes and an immobilized ﬁlm of lactateoxidase; during operation the enzyme catalyzes the oxidation of L-lactate and the electrodes are used to electrochemically detect either the
production of HzO2 or the consumption of ambient O2. Subcutaneousand intravenous lactate biosensors have been successfully demonstrated
in vivo using needle-type designs remarkably similar to implantableglucose biosensors (Baker and Gough, 1995; Hu et al., 1993). Demonstrated devices exhibit sub-millimolar detection limits, rapid response
times, and dynamic ranges exceeding the typical concentrations of interest (0.5–5 mM). Given the success of the underlying technology, thesimilarities to commercialized glucose-monitors, and the relevant
medical application, lactate biosensors seem the obvious choice for the
next long-indwelling closed-loop system.
However, to date there have been no clinical human trials withimplantable lactate biosensors, let alone signiﬁcant steps towards a
closed-loop system. This is in part due to the diﬃculty correlating
measurements of lactate in interstitial ﬂuid with blood concentrations.
Guiseppi-Elie (2011) and Rong et al. (2008) both observed discordance
between subcutaneous lactate sensor measurements and conventional
blood measurements, while Spehar-Deleze et al. (2012) reported noagreement between their subcutaneous lactate sensor and blood measurement controls. Long indwelling intravenous sensors remain too
risky for chronic use due to possibility of thrombosis. More recently
investigators have explored alternative approaches targeting sweat,tears, and saliva, using novel sensor packaging incorporated into epidermal bands (Imani et al., 2016), contact lenses (Thomas et al., 2012),
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The next several years will witness rapid commercialization ofclosed-loop drug control, driven by a focus on diabetes therapy. We
expect commercialization to spur a positive feedback in research efforts: market demand will fuel interest and funding for newer technologies, and commercial systems will provide a convenient platform
for extended clinical studies on the therapeutic gains under closed-loop
control. Successful adoption by patients and positive results fromclinical and epidemiological study may lead to greater interest in
closed-loop systems outside of the narrow purview of diabetes treatment.

growing prevalence of at-home and even ambulatory infusion pumps
for chemotherapy treatments echoes the history of diabetes treatment,
which ultimately led to the convenience of implantable biosensors integrated with personal drug delivery devices.
4.2.3. Neurotransmitters
Implantable biosensors have application for real-time detection ofneurotransmitters, and are of particular use for detecting non-electrooxidizable species which cannot be detected using cyclic voltammetry(e.g. L-glutamate or acetylcholine) (Chandra et al., 2017). The typicalbiosensor design consists of a modiﬁed electrophysiological recording
microelectrode with an immobilized matrix of an oxidoreductase enzyme coating an electrode recording site, enabling speciﬁc amperometric detection for species of interest. These neural biosensor probes
exhibit sub-micromolar sensitivities, adequate for monitoring extracellular concentrations of neurotransmitters, and are primarily used infundamental neuroscience and behavioral research.
In recent years, investigators studying neurotransmitter levels in
response to therapeutic deep brain stimulation (DBS) have begun usingneural biosensors for closed-loop control of neurotransmitter levels. Inan early example, Behrend et al. (2009) demonstrated an ability tocontrol in vivo L-glutamate levels using high frequency subthalamicstimulation in rats. L-glutamate was measured with a glutamate oxidase
linked amperometric probe, and provided feedback for an empiricallydetermined transfer function which modulated stimulation. L-glutamate
levels were controlled dynamically in vivo for several hours. In subsequent work, several investigators created closed-loop DBS systems
exploiting voltammetric detection of dopamine, serotonin, and adenosine using carbon ﬁber microelectrodes (Araujo et al., 2015;
Bozorgzadeh et al., 2016; Grahn et al., 2014; Lee et al., 2017). Theseeﬀorts have established the viability of closed-loop control over neurochemical levels, albeit with electrophysiological modulation (DBS)
instead of pharmaceutical modulation (drug-delivery). With recent
developments of neurochemical sensors integrating direct drug delivery
capabilities (Gibson, 2011; Rohatgi et al., 2009), the potential for
closed-loop drug delivery systems in the brain for modulation of psychological conditions is growing.

Acknowledgments
The authors would like to acknowledge the Viterbi School ofEngineering for support of Dr. Kee Scholten.
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