Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation

The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. Despite advancements in machine learning applications for this purpose, the specific contribution of in-silico biomarkers to toxicity risk levels has yet to be thoroughly elucidated. This study addresses this gap by implementing explainable artificial intelligence (XAI) to illuminate the impact of individual biomarkers in drug toxicity prediction. We employed the Markov chain Monte Carlo method to generate a detailed dataset for 28 drugs, from which twelve in-silico biomarkers of 12 drugs were computed to train various machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), XGBoost, K-Nearest Neighbors (KNN), and Radial Basis Function (RBF) networks. Our study's innovation is leveraging XAI, mainly through the SHAP (SHapley Additive exPlanations) method, to dissect and quantify the contributions of biomarkers across these models. Furthermore, the model performance was evaluated using the test set from 16 drugs. We found that the ANN model coupled with the eleven most influential in-silico biomarkers namely <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mfrac><mml:mrow><mml:mi>dVm</mml:mi></mml:mrow> <mml:mrow><mml:mi>dt</mml:mi></mml:mrow> </mml:mfrac> <mml:mrow><mml:mi>repol</mml:mi></mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mfrac><mml:mrow><mml:mi>dVm</mml:mi></mml:mrow> <mml:mrow><mml:mi>dt</mml:mi></mml:mrow> </mml:mfrac> <mml:mrow><mml:mi>max</mml:mi></mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>APD</mml:mi></mml:mrow> <mml:mn>90</mml:mn></mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>APD</mml:mi></mml:mrow> <mml:mn>50</mml:mn></mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>APD</mml:mi></mml:mrow> <mml:mrow><mml:mi>tri</mml:mi></mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>CaD</mml:mi></mml:mrow> <mml:mn>90</mml:mn></mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>CaD</mml:mi></mml:mrow> <mml:mn>50</mml:mn></mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>Ca</mml:mi></mml:mrow> <mml:mrow><mml:mi>tri</mml:mi></mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:msub><mml:mrow><mml:mi>Ca</mml:mi></mml:mrow> <mml:mrow><mml:mi>Diastole</mml:mi></mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:mi>q</mml:mi> <mml:mi>I</mml:mi> <mml:mi>n</mml:mi> <mml:mi>w</mml:mi> <mml:mi>a</mml:mi> <mml:mi>r</mml:mi> <mml:mi>d</mml:mi> <mml:mo>,</mml:mo> <mml:mi>a</mml:mi> <mml:mi>n</mml:mi> <mml:mi>d</mml:mi> <mml:mi>q</mml:mi> <mml:mi>N</mml:mi> <mml:mi>e</mml:mi> <mml:mi>t</mml:mi></mml:mrow> </mml:math> showed the highest classification performance among all classifiers with Area Under the Curve (AUC) scores of 0.92 for predicting high-risk, 0.83 for intermediate-risk, and 0.98 for low-risk drugs. We also found that the optimal in silico biomarkers selected based on SHAP analysis may be different for various classification models. However, we also found that the biomarker selection only sometimes improved the performance; therefore, evaluating various classifiers is still essential to obtain the desired classification performance. Our proposed method could provide a systematic way to assess the best classifier with the optimal in-silico biomarkers for predicting the TdP risk of drugs, thereby advancing the field of cardiac safety evaluations.