ArticleToxicology and applied pharmacology2020
Combining an in silico proarrhythmic risk assay with a tPKPD model to predict QTc interval prolongation in the anesthetized guinea pig assay.
Article in Toxicology and applied pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network.Computational and mathematical methods in medicine · 2022Article
- Emerging technology and platforms for cardiotoxicity testing.Toxicology and applied pharmacology · 2020Article
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13 authors.
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Abstract
Human-based in silico models are emerging as important tools to study the effects of integrating inward and outward ion channel currents to predict clinical proarrhythmic risk. The aims of this study were 2-fold: 1) Evaluate the capacity of an in silico model to predict QTc interval prolongation in the in vivo anesthetized cardiovascular guinea pig (CVGP) assay for new chemical entities (NCEs) and; 2) Determine if a translational pharmacokinetic/pharmacodynamic (tPKPD) model can improve the predictive capacity. In silico simulations for NCEs were performed using a population of human ventricular action potential (AP) models. PatchXpress® (PX) or high throughput screening (HTS) ion channel data from respectively n = 73 and n = 51 NCEs were used as inputs for the in silico population. These NCEs were also tested in the CVGP (n = 73). An M5 pruned decision tree-based regression tPKPD model was used to evaluate the concentration at which an NCE is liable to prolong the QTc interval in the CVGP. In silico results successfully predicted the QTc interval prolongation outcome observed in the CVGP with an accuracy/specificity of 85%/73% and 75%/77%, when using PX and HTS ion channel data, respectively. Considering the tPKPD predicted concentration resulting in QTc prolongation (EC
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