Comprehensive In Vitro Proarrhythmia Assay Research Articles

Development of in-silico drug cardiac toxicity evaluation system with consideration of inter-individual variability.

Safety pharmacology examines the potential for new drugs to have unusual, rare side effects such as torsade de pointes (TdP). Recently, as a part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) project, techniques for predicting the development of drug-induced TdP through computer simulations have been proposed and verified. However, CiPA assessment generally does not consider the effect of cardiac cell inter-individual variability, especially related to metabolic status. The study aimed to explore whether rare proarrhythmic effects may be linked to the inter-individual variability of cardiac cells and whether incorporating this variability into computational models could alter the prediction of drugs' TdP risks. This study evaluated the contribution of two biological characteristics to the proarrhythmic effects. The first was spermine concentration, which varies with metabolic status; the second was L-type calcium permeability that could occur due to mutations. Twenty-eight drugs were examined throughout this study, and qNet was analyzed as an essential feature. Even though there were some discrepancies of TdP risk predictions from the baseline model, we found that considering the inter-individual variability might change the TdP risk of drugs. Several drugs in the high-risk drugs group were predicted to affect as intermediate and low-risk drugs in some individuals and vice versa. Also, most intermediate-risk drugs were expected to act as low-risk drugs. When compared, the effects of inter-individual variability of L-type calcium were more significant than spermine in altering the TdP risk of compounds. These results emphasize the importance of considering inter-individual variability to assess drugs.

Identification and characterisation of functional Kir6.1-containing ATP-sensitive potassium channels in the cardiac ventricular sarcolemmal membrane.

The canonical Kir6.2/SUR2A ventricular KATP channel is highly ATP-sensitive and remains closed under normal physiological conditions. These channels activate only when prolonged metabolic compromise causes significant ATP depletion and then shortens the action potential to reduce contractile activity. Pharmacological activation of KATP channels is cardioprotective, but physiologically, it is difficult to understand how these channels protect the heart if they only open under extreme metabolic stress. The presence of a second KATP channel population could help explain this. Here, we characterise the biophysical and pharmacological behaviours of a constitutively active Kir6.1-containing KATP channel in ventricular cardiomyocytes. Patch-clamp recordings from rat ventricular myocytes in combination with well-defined pharmacological modulators was used to characterise these newly identified K+ channels. Action potential recording, calcium (Fluo-4) fluorescence measurements and video edge detection of contractile function were used to assess functional consequences of channel modulation. Our data show a ventricular K+ conductance whose biophysical characteristics and response to pharmacological modulation were consistent with Kir6.1-containing channels. These Kir6.1-containing channels lack the ATP-sensitivity of the canonical channels and are constitutively active. We conclude there are two functionally distinct populations of ventricular KATP channels: constitutively active Kir6.1-containing channels that play an important role in fine-tuning the action potential and Kir6.2/SUR2A channels that activate with prolonged ischaemia to impart late-stage protection against catastrophic ATP depletion. Further research is required to determine whether Kir6.1 is an overlooked target in Comprehensive in vitro Proarrhythmia Assay (CiPA) cardiac safety screens.

HiPSC-CM Electrophysiology: Impact of Temporal Changes and Study Parameters on Experimental Reproducibility.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are frequently used for preclinical cardiotoxicity testing and remain an important tool for confirming model-based predictions of drug effects in accordance with Comprehensive in Vitro Proarrhythmia Assay (CiPA). Despite the considerable benefits hiPSC-CMs provide, concerns surrounding experimental reproducibility have emerged. We investigated the effects of temporal changes and experimental parameters on hiPSC-CM electrophysiology. iCell cardiomyocytes2 were cultured and biosignals were acquired using a microelectrode array (MEA) system (1-14 days). Continuous recordings revealed a 22.6% increase in the beating rate and 7.7% decrease in the field potential duration (FPD) during a 20-minute equilibration period. Location specific differences across a multiwell plate were also observed, with iCell cardiomyocytes2 in the outer rows beating 8.8 beats per minute (BPM) faster than the inner rows. Cardiac endpoints were also impacted by cell culture duration; from 2-14 days the beating rate decreased (-12.7 BPM), FPD lengthened (+257 ms), and spike amplitude increased (+3.3 mV). Cell culture duration (4-10 days) also impacted cardiomyocyte drug responsiveness (E-4031, nifedipine, isoproterenol). qRT-PCR results suggest that daily variations in cardiac metrics may be linked to the continued maturation of hiPSC-CMs in culture (2-30 days). Daily experiments were also repeated using a second cell line (Cor.4U). Collectively, our study highlights multiple sources of variability to consider and address when performing hiPSC-CM MEA studies. To improve reproducibility and data interpretation, MEA-based studies should establish a standardized protocol and report key experimental conditions (e.g., cell line, culture time, equilibration time, electrical stimulation settings, raw data values).

Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output.

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.

In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations.

In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.

HiPSCs and organoids: prediction of arrhythmogenic risks for optimized traditional Chinese medicine

Accurate assessment of the risks associated with traditional Chinese medicine(TCM), such as the potential to induce serious cardiovascular adverse reactions including cardiac arrhythmias, is crucial. This article introduced the pharmacological evaluation strategies for cardiac safety and the progress in cardiac organ research, with a focus on discussing the application prospects of human induced pluripotent stem cells(hiPSCs) and organoids in assessing the risks of TCM-induced cardiac arrhythmias. Compared with traditional animal models, hiPSCs and organoid models provide better reference and predictive capabilities, allowing for more accurate simulation of human cardiac responses. Researchers have successfully generated various cardiac tissue models that mimic the structure and function of the heart to evaluate the effects of TCM on the heart. The hiPSCs model, by reprogramming adult cells into pluripotent stem cells and differentiating them into cardiac cells, enables the generation of personalized cardiac tissue, which better reflects individual differences and drug responses. This provides guidance for the assessment of TCM cardiac toxicity risks. By combining organoid model with cardiac safety pharmacology strategies such as electrocardiogram monitoring and ion channel function assessment, the impact of TCM on the heart can be comprehensively evaluated. In addition, the application of the Comprehensive in Vitro Proarrhythmia Assay(CiPA) approach improves the accuracy of evaluation. Applying the CiPA approach to TCM research reveals potential risks and provides a scientific basis for the clinical application and industrial development of TCM. In conclusion, organoid model and cardiac safety pharmacology evaluation strategies provide important tools for assessing the cardiac toxicity risks of TCM. The combination of hiPSCs model, comprehensive assessment methods, and the CiPA strategy enables an accurate assessment of the risks of TCM-induced cardiac arrhythmias, thus providing a scientific basis for the safe use and international recognition of TCM in clinical practice. This contributes to ensuring the safety and efficacy of TCM and promoting its clinical application and global acceptance.

Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive in vitro proarrhythmia assay (CiPA).

Due to the outbreak of the SARS-CoV-2 virus, drug repurposing and Emergency Use Authorization have been proposed to treat the coronavirus disease 2019 (COVID-19) during the pandemic. While the efficiency of the drugs has been discussed, it was identified that certain compounds, such as chloroquine and hydroxychloroquine, cause QT interval prolongation and potential cardiotoxic effects. Drug-induced cardiotoxicity and QT prolongation may lead to life-threatening arrhythmias such as torsades de pointes (TdP), a potentially fatal arrhythmic symptom. Here, we evaluated the risk of repurposed pyronaridine or artesunate-mediated cardiac arrhythmias alone and in combination for COVID-19 treatment through in vitro and in silico investigations using the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative. The potential effects of each drug or in combinations on cardiac action potential (AP) and ion channels were explored using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and Chinese hamster ovary (CHO) cells transiently expressing cardiac ion channels (Nav1.5, Cav1.2, and hERG). We also performed in silico computer simulation using the optimized O'Hara-Rudy human ventricular myocyte model (ORd model) to classify TdP risk. Artesunate and dihydroartemisinin (DHA), the active metabolite of artesunate, are classified as a low risk of inducing TdP based on the torsade metric score (TMS). Moreover, artesunate does not significantly affect the cardiac APs of hiPSC-CMs even at concentrations up to 100 times the maximum serum concentration (Cmax). DHA modestly prolonged at APD90 (10.16%) at 100 times the Cmax. When considering Cmax, pyronaridine, and the combination of both drugs (pyronaridine and artesunate) are classified as having an intermediate risk of inducing TdP. However, when considering the unbound concentration (the free fraction not bound to carrier proteins or other tissues inducing pharmacological activity), both drugs are classified as having a low risk of inducing TdP. In summary, pyronaridine, artesunate, and a combination of both drugs have been confirmed to pose a low proarrhythmogenic risk at therapeutic and supratherapeutic (up to 4 times) free Cmax. Additionally, the CiPA initiative may be suitable for regulatory use and provide novel insights for evaluating drug-induced cardiotoxicity.

Aging Model for Analyzing Drug-Induced Proarrhythmia Risks Using Cardiomyocytes Differentiated from Progeria-Patient-Derived Induced Pluripotent Stem Cells.

Among various cardiac safety concerns, proarrhythmia risks, including QT prolongation leading to Torsade de Pointes, is one of major cause for drugs being withdrawn (~45% 1975-2007). Preclinical study requires the evaluation of proarrhythmia using in silico, in vitro, and/or animal models. Considering that the primary consumers of prescription drugs are elderly patients, applications of "aging-in-a-dish" models would be appropriate for screening proarrhythmia risks. However, acquiring such models, including cardiomyocytes (CMs) derived from induced pluripotent stem cells (iPSCs), presents extensive challenges. We proposed the hypothesis that CMs differentiated from iPSCs derived from Hutchinson-Gilford progeria syndrome (HGPS, progeria) patients, an ultra-rare premature aging syndrome, can mimic the phenotypes of aging CMs. Our objective, therefore, was to examine this hypothesis by analyzing the response of 11 reference compounds utilized by the Food and Drug Administration (FDA)'s Comprehensive in vitro Proarrhythmia Assay (CiPA) using progeria and control CMs. As a sensitive surrogate marker of modulating cardiac excitation-contraction coupling, we evaluated drug-induced changes in calcium transient (CaT). We observed that the 80% CaT peak duration in the progeria CMs (0.98 ± 0.04 s) was significantly longer than that of control CMs (0.70 ± 0.05 s). Furthermore, when the progeria CMs were subjected to four doses of 11 compounds from low-, intermediate-, and high-risk categories, they demonstrated greater arrhythmia susceptibility than control cells, as shown through six-parameter CaT profile analyses. We also employed the regression analysis established by CiPA to classify the 11 reference compounds and compared proarrhythmia susceptibilities between the progeria and control CMs. This analysis revealed a greater proarrhythmia susceptibility in the progeria CMs compared to the control CMs. Interestingly, in both CMs, the compounds categorized as low risk did not exceed the safety risk threshold of 0.8. In conclusion, our study demonstrates increased proarrhythmia sensitivity in progeria CMs when tested with reference compounds. Future studies are needed to analyze underlying mechanisms and further validate our findings using a larger array of reference compounds.

Investigating the relevance of CYP2J2 inhibition for drugs known to cause intermediate to high risk torsades de pointes

Cardiac cytochrome P450 2J2 (CYP2J2) metabolizes endogenous polyunsaturated fatty acid, arachidonic acid (AA), to bioactive regioisomeric epoxyeicosatrienoic acid (EET) metabolites. This endogenous metabolic pathway has been postulated to play a homeostatic role in cardiac electrophysiology. However, it is unknown if drugs that cause intermediate to high risk torsades de pointes (TdP) exhibit inhibitory effects against CYP2J2 metabolism of AA to EETs. In this study, we demonstrated that 11 out of 16 drugs screened with intermediate to high risk of TdP as defined by the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative are concurrently reversible inhibitors of CYP2J2 metabolism of AA, with unbound inhibitory constant (Ki,AA,u) values ranging widely from 0.132 to 19.9 µM. To understand the physiological relevancy of Ki,AA,u, the in vivo unbound drug concentration within human heart tissue (Cu,heart) was calculated via experimental determination of in vitro unbound partition coefficient (Kpuu) for 10 CYP2J2 inhibitors using AC16 human ventricular cardiomyocytes as well as literature-derived values of fraction unbound in plasma (fu,p) and plasma drug concentrations in clinical scenarios leading to TdP. Notably, all CYP2J2 inhibitors screened belonging to the high TdP risk category, namely vandetanib and bepridil, exhibited highest Kpuu values of 18.2 ± 1.39 and 7.48 ± 1.16 respectively although no clear relationship between Cu,heart and risk of TdP could eventually be determined. R values based on basic models of reversible inhibition as per FDA guidelines were calculated using unbound plasma drug concentrations (Cu,plasma) and adapted using Cu,heart which suggested that 4 out of 10 CYP2J2 inhibitors with intermediate to high risk of TdP demonstrate greatest potential for clinically relevant in vivo cardiac drug-AA interactions. Our results shed novel insights on the relevance of CYP2J2 inhibition in drugs with risk of TdP. Further studies ascertaining the role of CYP2J2 metabolism of AA in cardiac electrophysiology, characterizing inherent cardiac ion channel activities of drugs with risk of TdP as well as in vivo evidence of drug-AA interactions will be required prior to determining if CYP2J2 inhibition could be an alternative mechanism contributing to drug-induced TdP.

In silico assessment on TdP risks of drug combinations under CiPA paradigm

Researchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines’ TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when limited drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models on several ion channels for various drug pairs. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as biomarker features. The drug combination maps were also used to illustrate combined medicines' TdP risk. We found that the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.

Improving the hERG model fitting using a deep learning-based method.

The hERG channel is one of the essential ion channels composing the cardiac action potential and the toxicity assay for new drug. Recently, the comprehensive in vitro proarrhythmia assay (CiPA) was adopted for cardiac toxicity evaluation. One of the hurdles for this protocol is identifying the kinetic effect of the new drug on the hERG channel. This procedure included the model-based parameter identification from the experiments. There are many mathematical methods to infer the parameters; however, there are two main difficulties in fitting parameters. The first is that, depending on the data and model, parametric inference can be highly time-consuming. The second is that the fitting can fail due to local minima problems. The simplest and most effective way to solve these issues is to provide an appropriate initial value. In this study, we propose a deep learning-based method for improving model fitting by providing appropriate initial values, even the right answer. We generated the dataset by changing the model parameters and trained our deep learning-based model. To improve the accuracy, we used the spectrogram with time, frequency, and amplitude. We obtained the experimental dataset from https://github.com/CardiacModelling/hERGRapidCharacterisation. Then, we trained the deep-learning model using the data generated with the hERG model and tested the validity of the deep-learning model with the experimental data. We successfully identified the initial value, significantly improved the fitting speed, and avoided fitting failure. This method is useful when the model is fixed and reflects the real data, and it can be applied to any in silico model for various purposes, such as new drug development, toxicity identification, environmental effect, etc. This method will significantly reduce the time and effort to analyze the data.

Testing the nonclinical Comprehensive In Vitro Proarrhythmia Assay (CiPA) paradigm with an established anti-seizure medication: Levetiracetam case study.

Levetiracetam (LEV), a well-established anti-seizure medication (ASM), was launched before the original ICH S7B nonclinical guidance assessing QT prolongation potential and the introduction of the Comprehensive In Vitro Proarrhythmia Assay (CiPA) paradigm. No information was available on its effects on cardiac channels. The goal of this work was to "pressure test" the CiPA approach with LEV and check the concordance of nonclinical core and follow-up S7B assays with clinical and post-marketing data. The following experiments were conducted with LEV (0.25-7.5 mM): patch clamp assays on hERG (acute or trafficking effects), NaV 1.5, CaV 1.2, Kir 2.1, KV 7.1/mink, KV 1.5, KV 4.3, and HCN4; in silico electrophysiology modeling (Virtual Assay® software) in control, large-variability, and high-risk human ventricular cell populations; electrophysiology measurements in human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes and dog Purkinje fibers; ECG measurements in conscious telemetered dogs after single oral administration (150, 300, and 600 mg/kg). Except a slight inhibition (<10%) of hERG and KV 7.1/mink at 7.5 mM, that is, 30-fold the free therapeutic plasma concentration (FTPC) at 1500 mg, LEV did not affect any other cardiac channels or hERG trafficking. In both virtual and real human cardiomyocytes, and in dog Purkinje fibers, LEV induced no relevant changes in electrophysiological parameters or arrhythmia. No QTc prolongation was noted up to 2.7 mM unbound plasma levels in conscious dogs, corresponding to 10-fold the FTPC. Nonclinical assessment integrating CiPA assays shows the absence of QT prolongation and proarrhythmic risk of LEV up to at least 10-fold the FTPC and the good concordance with clinical and postmarketing data, although this does not exclude very rare occurrence of QT prolongation cases in patients with underlying risk factors.

Sophoridine manifests as a leading compound for anti-arrhythmia with multiple ion-channel blocking effects

BackgroundSophoridine (SR) has shown the potential to be an antiarrhythmic agent. However, SR's electrophysiological properties and druggability research are relatively inadequate, which limits the development of SR as an antiarrhythmic candidate. PurposeTo facilitate the development process of SR as an antiarrhythmic candidate, we performed integrated studies on the electrophysiological properties of SR in vitro and ex vivo to gain more comprehensive insights into the multi-ion channel blocking effects of SR, which provided the foundation for the further drugability studies in antiarrhythmic and safety studies. Firstly, SR's electrophysiological properties and antiarrhythmic potentials were recorded and assessed at the cell and tissue levels by comprehensively integrating the patch clamp with the Electrical and Optical Mapping systems. Subsequently, the antiarrhythmic effects of SR were validated by aconitine and ouabain-induced arrhythmia in vivo. Finally, the safety of SR as an antiarrhythmic candidate compound was evaluated based on the guidelines of the Comprehensive in Vitro Proarrhythmia Assay (CiPA). Study designThe antiarrhythmic effect of SR was evaluated at the in vitro, ex vivo, and in vivo levels. MethodsIsolated primary cardiomyocytes and stable cell lines were prepared to explore the electrophysiologic properties of being a multiple ion-channel blocker in vitro by whole-cell patch clamp. Using electrical and optical mapping, the negative chronotropic effect of SR was determined in langendorff-perfused rat or guinea-pig hearts.The antiarrhythmic activity of SR was assessed by the ex vivo tachyarrhythmia models induced by left coronary artery ligation (LCAL) and isoproterenol (ISO). Canonical models of aconitine and ouabain-induced arrhythmia were used to verify the antiarrhythmic effects in vivo. Finally, the pro-arrhythmic risk of SR was detected in Human-Induced Pluripotent Stem Cell-Derived Cardiomyocytes (hSCCMs) using a Microelectrode array (MEA). ResultsSingle-cell patch assay validated the multiple ion-channel blockers of SR in transient outward current potassium currents (Ito), l-type calcium currents (ICa-l), and rapid activation delayed rectifier potassium currents (IKr). SR ex vivo depressed heart rates (HR) and ventricular conduction velocity (CV) and prolonged Q-T intervals in a concentration-dependent manner. Consistent with the changes in HRs, SR extended the active time of hearts and increased the action potential duration measured at 90% repolarization (APD90). SR could also significantly lengthen the onset time and curtail the duration of spontaneous ventricular tachycardia (VT) in the ex vivo arrhythmic model induced by LCAL. Meanwhile, SR could also significantly upregulate the programmed electrical stimulation (PES) frequency after the ISO challenge in forming electrical alternans and re-entrant excitation. Furthermore, SR exerted antiarrhythmic effects in the tachyarrhythmia models induced by aconitine and ouabain in vivo. Notably, the pro-arrhythmic risk of SR was shallow for a moderate inhibition of the human ether-à-go-go-related gene (hERG) channel. Moreover, SR prolonged field potential duration (FPDc) of hSCCMs in a concentration-dependent manner without early after depolarization (EAD) and arrhythmia occurrence. ConclusionOur results indicated that SR manifested as a multiple ion-channel blocker in the electrophysiological properties and exerts antiarrhythmic effects ex vivo and in vivo. Meanwhile, due to the low pro-arrhythmic risk in the hERG inhibition assay and the induction of EAD, SR has great potential as a leading candidate in the treatment of ventricular tachyarrhythmia.

Application of Convolutional Neural Networks Using Action Potential Shape for In-Silico Proarrhythmic Risk Assessment.

This study proposes a convolutional neural network (CNN) model using action potential (AP) shapes as input for proarrhythmic risk assessment, considering the hypothesis that machine-learning features automatically extracted from AP shapes contain more meaningful information than do manually extracted indicators. We used 28 drugs listed in the comprehensive in vitro proarrhythmia assay (CiPA), consisting of eight high-risk, eleven intermediate-risk, and nine low-risk torsadogenic drugs. We performed drug simulations to generate AP shapes using experimental drug data, obtaining 2000 AP shapes per drug. The proposed CNN model was trained to classify the TdP risk into three levels, high-, intermediate-, and low-risk, based on in silico AP shapes generated using 12 drugs. We then evaluated the performance of the proposed model for 16 drugs. The classification accuracy of the proposed CNN model was excellent for high- and low-risk drugs, with AUCs of 0.914 and 0.951, respectively. The model performance for intermediate-risk drugs was good, at 0.814. Our proposed model can accurately assess the TdP risks of drugs from in silico AP shapes, reflecting the pharmacokinetics of ionic currents. We need to secure more drugs for future studies to improve the TdP-risk-assessment robustness.

Calculations of the binding free energies of the Comprehensive in vitro Proarrhythmia Assay (CiPA) reference drugs to cardiac ion channels.

The evaluation of the inhibitory activities of drugs on multiple cardiac ion channels is required for the accurate assessment of proarrhythmic risks. Moreover, the in silico prediction of such inhibitory activities of drugs on cardiac channels can improve the efficiency of the drug-development process. Here, we performed molecular docking simulations to predict the complex structures of 25 reference drugs that were proposed by the Comprehensive in vitro Proarrhythmia Assay consortium using two cardiac ion channels, the human ether-a-go-go-related gene (hERG) potassium channel and human NaV1.5 (hNaV1.5) sodium channel, with experimentally available structures. The absolute binding free energy (ΔGbind) values of the predicted structures were calculated by a molecular dynamics-based method and compared with the experimental half-maximal inhibitory concentration (IC50) data. Furthermore, the regression analysis between the calculated values and negative of the common logarithm of the experimental IC50 values (pIC50) revealed that the calculated values of four and ten drugs deviated significantly from the regression lines of the hERG and hNaV1.5 channels, respectively. We reconsidered the docking poses and protonation states of the drugs based on the experimental data and recalculated their ΔGbind values. Finally, the calculated ΔGbind values of 24 and 19 drugs correlated with their experimental pIC50 values (coefficients of determination=0.791 and 0.613 for the hERG and hNaV1.5 channels, respectively). Thus, the regression analysis between the calculated ΔGbind and experimental IC50 data ensured the realization of an increased number of reliable complex structures.

QInward variability-based in-silico proarrhythmic risk assessment of drugs using deep learning model.

Many researchers have suggested evaluation methods and Torsades de Pointes (TdP) metrics to assess the proarrhythmic risk of a drug based on the in silico simulation, as part of the Comprehensive in-vitro Proarrhythmia Assay (CiPA) project. In the previous study, we validated the robustness of 12 in silico features using the ordinal logistic regression (OLR) model by comparing the classification performances of metrics according to the in-vitro experimental datasets used; however, the OLR model using 12 in silico features did not provide desirable results. This study proposed a convolutional neural network (CNN) model using the variability of promising in silico TdP metrics hypothesizing that the variability of in silico features based on beats has more information than the single value of in silico features. We performed the action potential (AP) simulation using a human ventricular myocyte model to calculate seven in silico features representing the electrophysiological cell states of drug effects over 1,000 beats: qNet, qInward, intracellular calcium duration at returning to 50% baseline (CaD50) and 90% baseline (CaD90), AP duration at 50% repolarization (APD50) and 90% repolarization (APD90), and dVm/dtMax_repol. The proposed CNN classifier was trained using 12 train drugs and tested using 16 test drugs among CiPA drugs. The torsadogenic risk of drugs was classified as high, intermediate, and low risks. We determined the CNN classifier by comparing the classification performance according to the variabilities of seven in silico biomarkers computed from the in silico drug simulation using the Chantest dataset. The proposed CNN classifier performed the best when using qInward variability to classify the TdP-risk drugs with 0.94 AUC for high risk and 0.93 AUC for low risk. In addition, the final CNN classifier was validated using the qInward variability obtained after merging three in-vitro datasets, but the model performance decreased to a moderate level of 0.75 and 0.78 AUC. These results suggest the need for the proposed CNN model to be trained and tested using various types of drugs.

Establishment and validation of a torsade de pointes prediction model based on human iPSC‑derived cardiomyocytes.

Drug-induced cardiotoxicity is one of the main causes of drug failure, which leads to subsequent withdrawal from pharmaceutical development. Therefore, identifying the potential toxic candidate in the early stages of drug development is important. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a useful tool for assessing candidate compounds for arrhythmias. However, a suitable model using hiPSC-CMs to predict the risk of torsade de pointes (TdP) has not been fully established. The present study aimed to establish a predictive TdP model based on hiPSC-CMs. In the current study, 28 compounds recommended by the Comprehensive in vitro Proarrhythmia Assay (CiPA) were used as training set and models were established in different risk groups, high- and intermediate-risk versus low-risk groups. Subsequently, six endpoints of electrophysiological responses were used as potential model predictors. Accuracy, sensitivity and area under the curve (AUC) were used as evaluation indices of the models and seven compounds with known TdP risk were used to verify model differentiation and calibration. The results showed that among the seven models, the AUC of logistic regression and AdaBoost model was higher and had little difference in both training and test sets, which indicated that the discriminative ability and model stability was good and excellent, respectively. Therefore, these two models were taken as submodels, similar weight was configured and a new TdP risk prediction model was constructed using a soft voting strategy. The classification accuracy, sensitivity and AUC of the new model were 0.93, 0.95 and 0.92 on the training set, respectively and all 1.00 on the test set, which indicated good discrimination ability on both training and test sets. The risk threshold was defined as 0.50 and the consistency between the predicted and observed results were 92.8 and 100% on the training and test sets, respectively. Overall, the present study established a risk prediction model for TdP based on hiPSC-CMs which could be an effective predictive tool for compound-induced arrhythmias.

Validation of in silico biomarkers for drug screening through ordinal logistic regression.

Since the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiation, many studies have suggested various in silico features based on ionic charges, action potentials (AP), or intracellular calcium (Ca) to assess proarrhythmic risk. These in silico features are computed through electrophysiological simulations using in vitro experimental datasets as input, therefore changing with the quality of in vitro experimental data; however, research to validate the robustness of in silico features for proarrhythmic risk assessment of drugs depending on in vitro datasets has not been conducted. This study aims to verify the availability of in silico features commonly used in assessing the cardiac toxicity of drugs through an ordinal logistic regression model and three in vitro datasets measured under different experimental environments and with different purposes. We performed in silico drug simulations using the Tomek-Ohara Rudy (ToR-ORD) ventricular myocyte model and computed 12 in silico features comprising six AP features, four Ca features, and two ion charge features, which reflected the effect and characteristics of each in vitro data for CiPA 28 drugs. We then compared the classific performances of ordinal logistic regressions according to these 12 in silico features and used in vitro datasets to validate which in silico feature is the best for assessing the proarrhythmic risk of drugs at high, intermediate, and low levels. All 12 in silico features helped determine high-risky torsadogenic drugs, regardless of the in vitro datasets used in the in silico simulation as input. In the three types of in silico features, AP features were the most reliable for determining the three Torsade de Pointes (TdP) risk standards. Among AP features, AP duration at 50% repolarization (APD50) was the best when individually using in silico features per in vitro dataset. In contrast, the AP repolarization velocity (dVm/dtMax_repol) was the best when merging all in silico features computed through three in vitro datasets.

Proarrhythmia Risk Assessment Using Electro-Mechanical Window in Human iPS Cell-Derived Cardiomyocytes.

Evaluation of drug-induced cardiotoxicity is still challenging to avoid adverse effects, such as torsade de pointes (TdP), in non-clinical and clinical studies. Numerous studies have suggested that human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a useful platform for detecting drug-induced TdP risks. Comprehensive in vitro Proarrhythmia Assay (CiPA) validation study suggested that hiPSC-CMs can assess clinical TdP risk more accurately than the human ether-a-go-go-related assay and QT interval prolongation. However, there were still some outliers, such as bepridil, mexiletine, and ranolazine, among the CiPA 28 compounds in the CiPA international multi-site study using hiPSC-CMs. In this study, we assessed the effects of the positive compound dofetilide, the negative compound aspirin, and several CiPA compounds (bepridil, mexiletine, and ranolazine) on the electromechanical window (E-M window), which were evaluated using multi-electrode array assay and motion analysis, in hiPSC-CMs. Similar to previous in vivo studies, dofetilide, which has a high TdP risk, decreased the E-M window in hiPSC-CMs, whereas aspirin, which has a low TdP risk, had little effect. Bepridil, classified in the high TdP-risk group in CiPA, decreased the E-M window in hiPSC-CMs, whereas ranolazine and mexiletine, which are classified in the low TdP-risk group in CiPA, slightly decreased or had little effect on the E-M window of hiPSC-CMs. Thus, the E-M window in hiPSC-CMs can be used to classify drugs into high and low TdP risk.

Proarrhythmic risk assessment of drugs by dVm /dt shapes using the convolutional neural network.

Comprehensive in vitro Proarrhythmia Assay (CiPA) projects for assessing proarrhythmic drugs suggested a logistic regression model using qNet as the Torsades de Pointes (TdP) risk assessment biomarker, obtained from in silico simulation. However, using a single in silico feature, such as qNet, cannot reflect whole characteristics related to TdP in the entire action potential (AP) shape. Thus, this study proposed a deep convolutional neural network (CNN) model using differential action potential shapes to classify three proarrhythmic risk levels: high, intermediate, and low, considering both characteristics related to TdP not only in the depolarization phase but also the repolarization phase of AP shape. We performed an in silico simulation and got AP shapes with drug effects using half‐maximal inhibitory concentration and Hill coefficients of 28 drugs released by CiPA groups. Then, we trained the deep CNN model with the differential AP shapes of 12 drugs and tested it with those of 16 drugs. Our model had a better performance for classifying the proarrhythmic risk of drugs than the traditional logistic regression model using qNet. The classification accuracy was 98% for high‐risk level drugs, 94% for intermediate‐risk level drugs, and 89% for low‐risk level drugs.