ஜர்னல் ஆஃப் பார்மகோஜெனோமிக்ஸ் & பார்மகோபுரோட்டியோமிக்ஸ்

சுருக்கம்

ToxTree: Descriptor Based Machine Learning Models to Predict hERG and Nav1.5 Cardiotoxicity

Issar Arab, Khaled Barakat

Drug-mediated blockade of the voltage-gated potassium channel (hERG) and the voltage-gated sodium channel (Nav1.5) can lead to severe cardiovascular complications. This rising concern has been reflected in the drug development arena, as the frequent emergence of cardiotoxicity from many approved drugs led to either discontinuing their use or, in some cases, their withdrawal from the market. Predicting potential hERG and Nav1.5 blockers at the outset of the drug discovery process can resolve this problem and can, therefore, decrease the time and expensive cost of developing safe drugs. One fast and cost-effective approach is to use in silico predictive methods to weed out potential hERG and Nav1.5 blockers at the early stages of drug development. Here, we introduce two robust 2D descriptor-based QSAR predictive models for both hERG and Nav1.5 liability predictions. The machine learning models were trained for both regression, predicting the potency value of a drug, and multiclass classification at three different potency cut-offs (i.e., 1 μM, 10 μM, and 30 μM), where ToxTree-hERG Classifier, a pipeline of Random Forest models, was trained on a large curated dataset of 8380 unique molecular compounds. Whereas ToxTree- Nav1.5 Classifier, a pipeline of kernelized SVM models, was trained on a large manually curated set of 1550 unique compounds retrieved from both ChEMBL and PubChem publicly available bioactivity databases. The hERG model yielded a multiclass accuracy of Q4=74.5% and a binary classification performance of Q2=93.2%, sensitivity=98.7%, specificity=75%, MCC=80.3%, and a CCR=86.8% on an external test set of N=499 compounds. The proposed inducer outperformed most metrics of the state-of-the-art published model and other existing tools. Additionally, we are introducing the first Nav1.5 liability predictive model achieving a Q4=74.9% and a binary classification of Q2=86.7% with MCC=71.2% and F1=89.7% evaluated on an external test set of 173 unique compounds. The curated datasets used in this project are made publicly available to the research community.