All-Optical Electrophysiology Refines Populations of In Silico Human iPSC-CMs for Drug Evaluation

Abstract

High-throughput in vitro drug assays have been impacted by recent advances in human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) technology and by contact-free all-optical systems simultaneously measuring action potentials (APs) and Ca 2+ transients (CaTrs). Parallel computational advances have shown that in silico simulations can predict drug effects with high accuracy. We combine these in vitro and in silico technologies and demonstrate the utility of high-throughput experimental data to refine in silico hiPSC-CM populations and to predict and explain drug action mechanisms. Optically obtained hiPSC-CM APs and CaTrs were used from spontaneous activity and under optical pacing in control and drug conditions at multiple doses. An updated version of the Paci2018 model was developed to refine the description of hiPSC-CM spontaneous electrical activity; a population of in silico hiPSC-CMs was constructed and calibrated using simultaneously recorded APs and CaTrs. We tested in silico five drugs (astemizole, dofetilide, ibutilide, bepridil, and diltiazem) and compared the outcomes to in vitro optical recordings. Our simulations showed that physiologically accurate population of models can be obtained by integrating AP and CaTr control records. Thus, constructed population of models correctly predicted the drug effects and occurrence of adverse episodes, even though the population was optimized only based on control data and in vitro drug testing data were not deployed during its calibration. Furthermore, the in silico investigation yielded mechanistic insights; e.g., through simulations, bepridil’s more proarrhythmic action in adult cardiomyocytes compared to hiPSC-CMs could be traced to the different expression of ion currents in the two. Therefore, our work 1) supports the utility of all-optical electrophysiology in providing high-content data to refine experimentally calibrated populations of in silico hiPSC-CMs, 2) offers insights into certain limitations when translating results obtained in hiPSC-CMs to humans, and 3) shows the strength of combining high-throughput in vitro and population in silico approaches.

Comparison of the Simulated Response of Three in Silico Human Stem Cell-Derived Cardiomyocytes Models and in Vitro Data Under 15 Drug Actions

“…The importance of I CaL in the first two models is related to the role of Ca 2+ handling and I NCX in sustaining the automaticity. As reported in (Koivumäki et al, 2018;Paci et al, 2020), one mechanism sustaining the automaticity is a small inward component of I NCX inducing the depolarization of the membrane potential until it reaches the activation threshold for I Na . After I CaL block, less Ca 2+ enters the cytosol and is available for the Ca 2+ -induced Ca 2+ release, and the Ca 2+ transient pre-upstroke component becomes smaller and smaller and this reduces the activity of I NCX that cannot support anymore proper automaticity.…”

“…At the time of the publication of the first in silico hSC-CM model (Paci et al, 2013), a limited amount of in vitro data was available in spite of the seminal work by Ma et al (Ma et al, 2011), that represented the most complete characterization of ion currents and APs from control hSC-CMs. Due to the growing interest in the hSC-CM technology and its applications, additional experimental data were published, enabling the development of more sophisticated hSC-CM models (Paci et al, 2018b;Paci et al, 2020;Koivumäki et al, 2018;Kernik et al, 2019). The first Paci model, Paci 2013(Paci et al, 2013, was developed entirely based on the Ma et al (Ma et al, 2011) dataset (Ma 2011) and additional in vitro data from cardiomyocytes derived from human embryonic stem cells for the ion currents missing in the Ma et al dataset.…”

“…We expect the insights from our study will enable other researchers to select the most appropriate model for their specific investigations. Table 1 shows the number of compartments and the ion currents, pumps and exchangers included in the three considered computational (in silico) cellular models: Kernik2019 (Kernik et al, 2019), Koivumäki2018 (Koivumäki et al, 2018), and Paci2020 (Paci et al, 2019;Paci et al, 2020). They are all single cell models, with a similar formulation for certain ionic currents, as they are all based on the original hSC-CM model from Paci et al (Paci et al, 2013).…”

“…In silico hSC-CM models also helped to gain insight on specific features of hSC-CMs, e.g. automaticity and its causes ( Koivumäki et al, 2018 ; Paci et al, 2020 ), and to tackle the huge electrophysiological variability observed in action potentials (AP) and Ca 2+ transients (CaT) recorded in vitro ( Paci et al, 2017 ; Kernik et al, 2019 ), e.g. showing potential combination of ion currents that could make cardiac cells more prone to develop arrhythmic behaviors in response to drugs or in presence of disease phenotypes ( Paci et al, 2017 ; Paci et al, 2018a ; Kernik et al, 2020 ).…”

In vitro and In silico Models to Study SARS-CoV-2 Infection: Integrating Experimental and Computational Tools to Mimic “COVID-19 Cardiomyocyte”

“…Similarly to the case of experimental models, there are peculiarities and limitations for modeling cardiomyocytes in silico ( Gong et al, 2017 ). However, in silico models have the flexibility of being easily adapted to new experimental data, such as the ones obtained from hiPSC-CMs, allowing for more accurate quantitative predictions ( Lei et al, 2017 ; Jæger et al, 2020 ; Paci et al, 2020 ). Furthermore, the most recent mechanistic models permit an improved translation of electrophysiological findings from hiPSC-CMs to human adult myocytes at both single-cell and tissue level ( Gong and Sobie, 2018 ; Tveito et al, 2018 ).…”