Exploring mutation specific beta blocker pharmacology of the pathogenic late sodium channel current from patient-specific pluripotent stem cell myocytes derived from long QT syndrome mutation carriers

Comollo, Thomas; Zou, Xinle; Zhang, Chuangeng; Kesters, Divya; Hof, Thomas; Sampson, Kevin J.; Kass, Robert S.

doi:10.1080/19336950.2022.2106025

Cited by 2 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, cardiotoxicity is the main reason for the failure of clinical candidates or post-market withdrawal ( Klon, 2010) . Genetically pharmacological studies evidence the causalities to cardiotoxicity is the human ether-à-go-go-related gene (hERG) ion channel protein expressed in the cardiomyocyte ( Comollo et al. (2022) ; Warmke and Ganetzky (1994) ).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, cardiotoxicity is the main reason for the failure of clinical candidates or postmarket withdrawal (Klon, 2010). Genetically pharmacological studies evidence the causalities to cardiotoxicity is the human etherà-go-go-related gene (hERG) ion channel protein expressed in the cardiomyocyte (Comollo et al (2022); Warmke and Ganetzky (1994)). The hERG gene encodes a voltage-gated potassium channel, which is a key component in the formation of the cardiac action potential by activating the delayed rectifier potassium current rapidly (Cavalluzzi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme

et al. 2023

View full text Add to dashboard Cite

Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure–activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure–activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question.Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted.Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: <1μM; non-active: >30μM). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.

show abstract

Section: Introductionmentioning

confidence: 99%