Dynamic monitoring of beating periodicity of stem cell‐derived cardiomyocytes as a predictive tool for preclinical safety assessment

Abstract

BACKGROUND AND PURPOSECardiac toxicity is a major concern in drug development and it is imperative that clinical candidates are thoroughly tested for adverse effects earlier in the drug discovery process. In this report, we investigate the utility of an impedance-based microelectronic detection system in conjunction with mouse embryonic stem cell-derived cardiomyocytes for assessment of compound risk in the drug discovery process. EXPERIMENTAL APPROACHBeating of cardiomyocytes was measured by a recently developed microelectronic-based system using impedance readouts. We used mouse stem cell-derived cardiomyocytes to obtain dose-response profiles for over 60 compounds, including ion channel modulators, chronotropic/ionotropic agents, hERG trafficking inhibitors and drugs known to induce Torsades de Pointes arrhythmias. KEY RESULTSThis system sensitively and quantitatively detected effects of modulators of cardiac function, including some compounds missed by electrophysiology. Pro-arrhythmic compounds produced characteristic profiles reflecting arrhythmia, which can be used for identification of other pro-arrhythmic compounds. The time series data can be used to identify compounds that induce arrhythmia by complex mechanisms such as inhibition of hERG channels trafficking. Furthermore, the time resolution allows for assessment of compounds that simultaneously affect both beating and viability of cardiomyocytes. CONCLUSIONS AND IMPLICATIONSMicroelectronic monitoring of stem cell-derived cardiomyocyte beating provides a high throughput, quantitative and predictive assay system that can be used for assessment of cardiac liability earlier in the drug discovery process. The convergence of stem cell technology with microelectronic monitoring should facilitate cardiac safety assessment. AbbreviationsBRI, beating rhythm irregularity; hERG, human ether a go go; MEA, multi elelctrode array; mESCC, mouse embryonic stem cell

Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors

“…5I), corresponding to their slightly reduced beating rates upon APAP treatment (Fig. 5J), as reported previously (60).…”

Curdlan-Conjugated PLGA Nanoparticles Possess Macrophage Stimulant Activity and Drug Delivery Capabilities

“…This was followed by a period in which CI dropped to below control levels (~ 0.7) within 8 h possibly due to changes in the cell morphology and hence decrease in adhesion. The drop in CI for the NPs, unlike for C-PLGA polymer, was followed by gradual sustained increases in CI to form characteristic "spoon-shaped" profiles between 80 and 120 h. Such "spoon shaped" profiles are associated with intracellular Ca 2+ increases [34]. To determine any differences in Ca 2+ signaling between the PLGA NPs and C-PLGA NPs, the negative area under the curve (for the "spoon shaped" structure) was calculated with 1 as baseline at 100 µg/mL using Graphpad Prism® (San Diego, California, USA).…”

Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy

“…The beating profile contains more information than can be obtained by electrophysiology or fluorescence imaging as it integrates the effect of all the ionchannel involved and thus offer a signature that can be used to predict the effect of specific compounds. For instance inhibitors of hERG channel (the main class of channels assessed in cardio safety, which are involved in repolarization current) all results in a similar profile [7]. In addition, due to the non-invasive aspect of the measurements, both short-term and longterm effects of the monitored compounds can be assessed with DHM.…”

“…Therefore, researchers and pharmaceutical companies have to ensure that the effect of lead candidate compounds on cardiac function strictly satisfy safety criteria. Consequently, it is critical to establish more informative in vitro cardiotoxicity screens and data analysis algorithms at the early phases of drug development for preventing late stage failure [7][8][9].…”