A greedy classifier optimization strategy to assess ion channel blocking activity and pro-arrhythmia in hiPSC-cardiomyocytes

International audienceNovel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ionchannel blockers or to sufficiently predict the risk forTorsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour.The algorithm is based on the construction of a dictionary and a greedy optimization, leading to the definition of optimal classifiers. Finally,we present a numerical tool that can accurately predict compound-induced pro-arrhythmicrisk and involvement of sodium,calcium and potassium channels,based on hiPSC-CM field potentialdata

Contraction pressure analysis using optical imaging in normal and MYBPC3-mutated hiPSC-derived cardiomyocytes grown on matrices with tunable stiffness

Current in vivo disease models and analysis methods for cardiac drug development have been insufficient in providing accurate and reliable predictions of drug efficacy and safety. Here, we propose a custom optical flow-based analysis method to quantitatively measure recordings of contracting cardiomyocytes on polydimethylsiloxane (PDMS), compatible with medium-throughput systems. Movement of the PDMS was examined by covalently bound fluorescent beads on the PDMS surface, differences caused by increased substrate stiffness were compared, and cells were stimulated with β-agonist. We further validated the system using cardiomyocytes treated with endothelin-1 and compared their contractions against control and cells incubated with receptor antagonist bosentan. After validation we examined two MYBPC3-mutant patient-derived cell lines. Recordings showed that higher substrate stiffness resulted in higher contractile pressure, while beating frequency remained similar to control. β-agonist stimulation resulted in both higher beating frequency as well as higher pressure values during contraction and relaxation. Cells treated with endothelin-1 showed an increased beating frequency, but a lower contraction pressure. Cells treated with both endothelin-1 and bosentan remained at control level of beating frequency and pressure. Lastly, both MYBPC3-mutant lines showed a higher beating frequency and lower contraction pressure. Our validated method is capable of automatically quantifying contraction of hiPSC-derived cardiomyocytes on a PDMS substrate of known shear modulus, returning an absolute value. Our method could have major benefits in a medium-throughput setting.</p

A greedy classifier optimisation strategy to assess ion channel blocking activity and pro-arrhythmia in hiPSC-cardiomyocytes

Novel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ion channel blockers or to sufficiently predict the risk for Torsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour. The algorithm is based on the construction of a dictionary and a greedy optimisation, leading to the definition of optimal classifiers. Finally, we present a numerical tool that can accurately predict compound-induced pro-arrhythmic risk and involvement of sodium, calcium and potassium channels, based on hiPSC-CM field potential data. 2 Introduction 1 The Comprehensive in vitro Proarrhythmia Assay (CiPA) is an initiative for a new 2 paradigm in safety pharmacology to redefine the non-clinical evaluation of Torsade de 3 Pointes (TdP) [1-3]. 4 It aims to more precisely assess TdP risk in vitro by using a multifaceted approach 5 that combines in vitro evaluations of electrophysiologic responses in human-induced 6 pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and in silico models 7 providing reconstructions of drug effects on ventricular electrical activity [4, 5]. 8 Since CiPA, in vitro studies using hiPSC-CMs become an increasingly integrated 9 part of today's cardiac safety assessment. While encouraging, adequately predicting 10 TdP risk of unknown drugs based on in vitro studies alone is challenging. Besides, the 11 analysis of the large data sets derived from those studies is often far from being 12 automated. 13 The main focus of the present study is to address these issues by investigating a 14 computational tool that combines statistical analysis and machine learning approaches 15 (used in this context in [6]) to the mathematical modeling and the numerical 16 simulations (in silico experiments) of the drug effects on the field potential (FP) of 17 hiPSC-CMs obtained by multi-electrode array (MEA) technology

Serum supplemented culture medium masks hypertrophic phenotypes in human pluripotent stem cell derived cardiomyocytes

Cardiolog

Psychotropic medication in pregnancy and lactation and early development of exposed children

There is still limited knowledge about alterations of blood concentrations of psychotropic drugs during pregnancy, the transfer of psychotropic drugs into breastmilk and the effects on exposed children. We investigated changes in concentrations of psychopharmacological medication during pregnancy and lactation in serum and breastmilk at different time points in a naturalistic sample of 60 mothers and observed the development of the exposed children in the first 12 months. We found a decrease in serum concentrations from the first to the second trimester of amitriptyline, duloxetine, escitalopram, quetiapine and sertraline. Citalopram stayed rather stable during pregnancy, sertraline levels interestingly increased again from the second to the third trimester. High concentration-by-dose ratios in breastmilk were found for venlafaxine as well as lamotrigine, low for quetiapine and clomipramine. Similarly, clomipramine and quetiapine showed low milk/serum–penetration ratios. Regarding the birth outcome measures in children, we found no significant differences between in utero exposed compared to nonexposed newborns. There were no significant differences in the development in the first 12 months. Psychotropic medication in the peripartum needs a balancing of risks and benefits and a continuous therapeutic drug monitoring can be a guidance for clinicians to monitor drug alteration patterns, which are likely to occur due to physiological pregnancy-associated changes in pharmacokinetics. Accordingly, therapeutic drug monitoring can optimize a medication in pregnancy and lactation with the lowest effective dose