The search for successful anti-arrhythmia therapeutics is rooted in the voltage clamp and current clamp techniques, which have provided the mechanistic details behind the ionic membrane currents that compose the cardiac action potential (AP). While basic science has made great leaps in identifying and characterizing the basic factors involved in arrhythmia, the translation of these advances into successful therapies has been lackluster. Nonetheless, investigators have been using a combination of experimental and computational approaches to unravel the complex mechanisms underlying cardiac arrhythmia. Using this approach, experimental measurements, typically in single cells from mammalian hearts, are used to develop biophysically detailed mathematical models that can be scaled up to the tissue and whole-organ levels where arrhythmia occurs. Unlike experiments, computational modeling readily allows for the precise perturbation of particular parameters individually or in controlled combinations (simulating, e.g., the multifactorial nature of many disorders), but results are reliant on the accuracy of the model and its many components. The dynamic clamp technique is a merger between experimental and computational techniques that has been gaining traction as a hybrid method for elucidating arrhythmia mechanisms and possible therapeutics.

Traditional patch clamp protocols are typically static and predetermined, such as sequential voltage steps used to study membrane current dependencies. Dynamic clamp is an extension of patch clamp, where measurements from the cell are used to modify a continuously changing experimental protocol in a real-time feedback loop (Robinson and Kawai, 1993; Sharp et al., 1993). Earlier work has shown broad application—coupling of separate cardiomyocytes through an artificial gap junction (Tan and Joyner, 1990; Joyner et al., 1991; Spitzer et al., 1997; Verheijck et al., 1998; Zaniboni et al., 2000; Huelsing et al., 2001), injection of measured current from a transfected cell into a primary isolated myocyte (Berecki et al., 2005, 2006), antrhomorphization of mouse cardiac APs (Ahrens-Nicklas and Christini, 2009; Bot et al., 2012), and more recently in the study of cardiomyocyte coupling to unexcitable cells (McSpadden et al., 2012) and fibroblasts/myofibroblasts (Nguyen et al., 2012; Brown et al., 2016). The history of dynamic clamp has been reviewed in detail elsewhere (Prinz et al., 2004; Wilders, 2006; Ravagli et al., 2016). Here, we focus on a specific configuration of this technique, called the dynamic model clamp (referred hereafter as dynamic clamp), where a mathematically based model of a conductance is injected to the cell in real-time. Characteristically, this mathematical model describes a specific voltage and time-dependent membrane current determined by a set of differential equations. Measured voltage of a cell in a patch clamp configuration is fed into a mathematical model at high rates, from which the calculated current is injected back into the cell (Figure 1A).

Central to the dynamic clamp experimental rig is the software, which acts as the interface between the patch-clamp hardware and mathematical models. Accurate and rapid sampling of the membrane potential and computation of the virtual conductance is required to mimic sufficiently a biological conductance (Bettencourt et al., 2008). These requirements necessitate hard real-time control. In this context, the feedback loop must complete every iteration within a specified time constraint, typically 50–100 μs (10–20 kHz) in cardiomyocyte dynamic clamp experiments, a feat not possible on standard operating systems and software due to technical limitations. The works discussed here predominately use two software platforms—DynaClamp (Berecki et al., 2005, 2006) and the Real-Time eXperimental Interface (RTXI, www.rtxi.org; Ortega et al., 2014; Patel et al., 2017). Both platforms utilize a customized real-time Linux operating system and are freely available.

In this review, we discuss how investigators have used the dynamic clamp technique to test theoretical drug targets, validate and improve existing cardiac mathematical models, and design assays for cardiotoxicity testing.

Dynamic clamp has also been utilized in the development of new assays for assessment of drug proarrhythmic risks. The current regulatory framework used to prevent approval of drugs with the potential to induce TdP is focused on two main areas: the propensity of the drug to block the hERG channel in vitro, and whether the drug prolongs the QTc interval of the ECG. Though largely successful at preventing proarrhythmic drugs from entering the market, the approach has been criticized due to its low specificity, as hERG block and QT prolongation do not always carry torsadogenic risk (Sager et al., 2014; Colatsky et al., 2016). Consequently, it is generally agreed that many promising drugs that may have little arrhythmogenic risk have their development terminated due to failing either criteria. As mentioned previously, the CiPA initiative considers human stem-cell derived cardiomyocytes a key component in future drug safety assays (Sager et al., 2014; Colatsky et al., 2016), and dynamic clamp has been used to address key limitations.

Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are being used as an alternative to traditional animal models, cell lines, and heterologous expression systems in the study of cardiac electrophysiology mechanisms and drug-induced arrhythmia. Due to the inherent difficulty in obtaining human cardiac tissue for study, hiPSC-CMs may provide an accessible source of human cell lines and includes the additional capacity to produce patient-specific lines. However, as with human embryonic stem cell derived cardiomyocytes, hiPSC-CMs exhibit an immature phenotype. These cells are stereotypically characterized by spontaneous activity, elevated maximum diastolic potentials, low maximum upstroke velocity, and highly variable APD (Hoekstra et al., 2012). A major contributing factor for these issues is hiPSC-CMs lack of the inward rectifying K⁺ current (I_K1), which plays a major role maintaining a stable resting potential in quiescent cardiomyocytes (Doss et al., 2012). The lack of I_K1 is a cumulative issue, in that a generally depolarized membrane potential influences other cardiac currents, such as lowering the availability of fast Na⁺ channels due to inactivation, which reduces upstroke velocity.

Bett et al. implemented a dynamic clamp based approach to resolve the immaturity issue in hiPSC-CMs through the addition of a virtual I_K1 current (Bett et al., 2013). The original erratic AP morphology of hiPSC-CMs (Figure 2A) was transformed to an AP profile similar to those seen in adult human cardiomyocytes (Figure 2B), with a stable resting membrane potential and fast upstroke velocity. Seeking to test the impact of the dynamic clamp transformation in response to drug perturbation, hiPSC-CMs were exposed to the Ca²⁺ agonist BayK-8644 at room temperature. Without dynamic clamp, drug addition ceased spontaneous AP generation (Figure 2C) most likely due to BayK-8644 induced Ca²⁺ loading. This is in stark contrast to what is expected from ventricular cardiomyocytes in humans and other mammalian species, where an increase in depolarizing Ca²⁺ currents is expected to increase APD and abnormal activity, such as EADs. With I_K1 dynamic clamp, however, APD prolongation is evident in stimulated APs (Figure 2D). This illustrates that while hiPSC-CMs are sensitive to BayK-8644, lack of I_K1 can mask the relevance of drug effects.

Building upon this work, Putten et al. used multiple I_K1 models in their dynamic clamp experiments to examine the impact of varying degrees of rectification (Meijer van Putten et al., 2015), a biological feature of the I_K1 current due to differential expression of the channel (Kir2.x) subunits (Wang et al., 1998). Additionally, I_K1 channelopathies were investigated by modifying their Kir2.1 model to represent gain-of-function and loss-of-function mutations. The gain-of-function mutation was based on the E299V mutation associated with short QT syndrome 3, and the loss-of-function mutation was based on the heterozygous dominant-negative mutation in KCNJ2 associated with Andersen-Tawil syndrome. The top panel of Figure 2E plots the different current-voltage relationships of the modified models. The bottom panel of Figure 2E shows the corresponding APs when these models are used in the calculation of the virtual I_K1 current during dynamic clamp. Consistent with short QT, the gain-of-function mutation significantly decreased APD, while the loss-of-function had only a marginal effect.

More recently, hiPSC-CM studies augmented with I_K1 dynamic clamp have provided insight into cardiac abnormalities such as Brugada syndrome (Veerman et al., 2016), long QT syndrome (Rocchetti et al., 2017), and familial atrial fibrilliation (Marczenke et al., 2017). While ion channel dysfunction has been associated with Brugada Syndrome, mainly the cardiac fast Na⁺ current, Veerman et al. found no clear cellular electrophysiological abnormalities in patient-derived hiPSC-CMs, suggesting that other factors, such as fibrosis, could also be underlying mechanisms (Veerman et al., 2016). Rocchetti et al. recently studied hiPSC-CMs derived from a long QT patient carrying a heterozygous mutation in one of the three calmodulin encoding genes (Rocchetti et al., 2017). The patient-specific cells exhibited prolonged APD and failure to shorten with increased pacing rate, which the study linked to impairment of Ca²⁺-dependent inactivation of I_CaL. The I_CaL blocker verapamil reversed mutation-induced repolarization abnormalities. Marczenke et al. explored the role of mutations of the KCNA5 gene, encoding the channel responsible for the ultrarapid delayed rectifier K⁺ current, in familial atrial fibrillation (Marczenke et al., 2017). The authors generated a functional KCNA5 knockout hiPSC-CM line combining CRISPR/Cas9-mediated mutagenesis and atrial- or ventricular-specific differentiation through manipulation of retinoic acid signaling (Devalla et al., 2015). They observed a strictly atrial-specific disease phenotype, where atrial KCNA5 knockout hiPSC-CMs exhibited prolonged APD and EADs at low stimulation frequencies vs. insignificant changes in the ventricular variant. These works highlight the potential of hiPSC-CMs in cardiac patient-specific and subtype-specific disease modeling.

I_K1 dynamic clamp is becoming more common to hiPSC-CM studies to reduce variability in experimental metrics, eliminate spontaneity due to elevated resting membrane potential, and yield a more physiological relevant phenotype. Verkerk et al. systematically analyzed the impact of I_K1 dynamic clamp on AP characteristics in atrial and ventricular hiPSC-CMs, and provided an in-depth comparison of the methodology and experimental variability of the studies discussed above (Verkerk et al., 2017). While I_K1 dynamic clamp appears to reduce the variability of most AP parameters, enthusiasm of reducing the large experimental variability of hiPSC-CMs is tempered by the observation that APD variability is not affected. However, elimination of spontaneous depolarizations allows for stimulus at static frequencies, permitting investigation into rate-dependence. More importantly, static pacing reduces beat-to-beat variability, granting a greater ability to detect AP parameter changes. Verkerk et al. also investigated the impact of different mathematical formulations of the injected I_K1 current, by comparing the models used in several studies discussed previously (Bett et al., 2013; Meijer van Putten et al., 2015; Rocchetti et al., 2017). Not surprisingly, the parameter selection of I_K1 current density and kinetics can influence relevant AP metrics. Conversely, the flexibility inherent to model modification provides a means to tailor the I_K1 current to specific cell types, such as ventricular or atrial.

The low throughput of dynamic clamp is a major limitation to its use as part of a drug testing hiPSC-CM platform. Techniques to increase maturation and I_K1 density, such as 3D culturing (Lemoine et al., 2017) and adenovirus-mediated overexpression of I_K1 (Vaidyanathan et al., 2016), may circumvent the need for dynamic clamp, but are currently not widely used. Automated patch clamp offers a possible route to increase throughput, but brings a new set of issues, such as interfacing with proprietary equipment and the use of single suspended cells. In a promising recent advance, Goversen et al. have successfully combined I_K1 dynamic clamp with automated patch clamp of hiPSC-CMs, suggesting the feasibility of high-throughput application as a drug testing platform (Goversen et al., 2017).

In summary, dynamic clamp has been utilized in a number of exciting studies to address some of the inherent limitations of hiPSC-CMs, suggesting a promise as a component of safety pharmacology testing. Furthermore, the ability to modify the underlying mathematical models to examine channelopathies expands the capabilities of this platform.

By coupling mathematical models with biological experiments, dynamic clamp has provided a powerful tool in the search for potential anti-arrhythmic therapies through model-based perturbations, enhanced hiPSC-CMs as a platform for pharmacological safety testing, and used to clarify and improve mathematical models of cardiac electrophysiology. Dynamic clamp allows fine manipulation of numerous parameters like in-silico studies, but is performed in the context of experimental biology. This approach has enabled investigators to test theoretical perturbations in real-time and in live cells, and the power of this technique is represented by the broadness seen in the studies discussed here. It is expected dynamic clamp will continue to elucidate the mechanisms underlying cardiac arrhythmia and identify novel drug targets, and could evolve into a high-throughput assay, e.g., on automated patch clamp platforms to improve maturity of hiPSC-CMs.

FO, EG, TK-M, and DC all contributed to the planning, writing, and editing of the manuscript and figures contained herein.