Paci Michelangelo, Passini Elisa, Klimas Aleksandra, Severi Stefano, Hyttinen Jari, Rodriguez Blanca, Entcheva Emilia
BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Biophys J. 2020 May 19;118(10):2596-2611. doi: 10.1016/j.bpj.2020.03.018. Epub 2020 Apr 4.
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 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.
高通量体外药物检测受到人类诱导多能干细胞衍生心肌细胞（hiPSC-CM）技术的最新进展以及同时测量动作电位（AP）和钙瞬变（CaTrs）的非接触式全光学系统的影响。并行的计算进展表明，计算机模拟可以高精度预测药物效果。我们将这些体外和计算机技术相结合，证明了高通量实验数据在优化计算机模拟的hiPSC-CM群体以及预测和解释药物作用机制方面的实用性。在对照和多种剂量药物条件下，利用光学记录获得的hiPSC-CM的AP和CaTrs，包括自发活动以及光学起搏下的数据。开发了Paci2018模型的更新版本，以完善对hiPSC-CM自发电活动的描述；利用同时记录的AP和CaTrs构建并校准了一组计算机模拟的hiPSC-CM。我们在计算机模拟中测试了五种药物（阿司咪唑、多非利特、伊布利特、苄普地尔和地尔硫䓬），并将结果与体外光学记录进行比较。我们的模拟表明，通过整合AP和CaTr控制记录，可以获得生理上准确的模型群体。因此，构建的模型群体正确预测了药物效果和不良事件的发生，尽管该群体仅基于对照数据进行了优化，在校准过程中未使用体外药物测试数据。此外，计算机模拟研究得出了机制性见解；例如，通过模拟，与hiPSC-CM相比，苄普地尔在成年心肌细胞中更易引发心律失常的作用可追溯到两者离子电流表达的差异。因此，我们的工作1）支持全光学电生理学在提供高内涵数据以优化经实验校准的计算机模拟hiPSC-CM群体方面的实用性，2）深入了解将hiPSC-CM中获得的结果转化为人体研究时的某些局限性，3）展示了高通量体外方法与计算机模拟群体方法相结合的优势。
