Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure–Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at .

Introduction: Sodium-glucose cotransporter type 2 inhibitors (SGLT2i), gliflozins, play an emerging role for the treatment of heart failure with reduced left ventricular ejection fraction (HFrEF). Nevertheless, the effects of SGLT2i on ventricular remodeling and function have not been completely understood yet. Explainable artificial intelligence represents an unprecedented explorative option to clinical research in this field. Based on echocardiographic evaluations, we identified some key clinical responses to gliflozins by employing a machine learning approach.Methods: Seventy-eight consecutive diabetic outpatients followed for HFrEF were enrolled in the study. Using a random forests classification, a single subject analysis was performed to define the profile of patients treated with gliflozins. An explainability analysis using Shapley values was used to outline clinical parameters that mostly improved after gliflozin therapy and machine learning runs highlighted specific variables predictive of gliflozin response.Results: The five-fold cross-validation analyses showed that gliflozins patients can be identified with a 0.70 ± 0.03% accuracy. The most relevant parameters distinguishing gliflozins patients were Right Ventricular S'-Velocity, Left Ventricular End Systolic Diameter and E/e' ratio. In addition, low Tricuspid Annular Plane Systolic Excursion values along with high Left Ventricular End Systolic Diameter and End Diastolic Volume values were associated to lower gliflozin efficacy in terms of anti-remodeling effects.Discussion: In conclusion, a machine learning analysis on a population of diabetic patients with HFrEF showed that SGLT2i treatment improved left ventricular remodeling, left ventricular diastolic and biventricular systolic function. This cardiovascular response may be predicted by routine echocardiographic parameters, with an explainable artificial intelligence approach, suggesting a lower efficacy in case of advanced stages of cardiac remodeling.

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Background Sodium glucose cotransporter type 2 inhibitors (SGLT2i), also called gliflozins, are playing an emerging role for the treatment of heart failure with reduced left ventricle ejection fraction (HFrEF). However, the direct effects of SGLT2i on left and right ventricular remodeling and function have not been completely clarified. We therefore aimed to assess clinical response to gliflozins focusing on echocardiographic evaluation and identify any predictive factors with a machine learning approach. Methods Based on Random Forest, a robust and consolidated machine learning approach, we carried out a single subject analysis to evaluate to which extent patients treated with gliflozins can effectively be distinguished from patients undergoing non-gliflozins treatments. Besides, we carried out an eXplainability analysis using Shapley values to outline the clinical parameters which mostly took advantage by gliflozins. Finally, machine learning experiments were designed to highlight the presence of specific clinical patterns undermining the gliflozins effectiveness. Results 5-fold cross-validation analyses showed that gliflozin treatment was identified with a 0.70 ± 0.03% accuracy; the most important parameters supporting such accuracy were Right Ventricle S’ Velocity (RV S’), Left Ventricle End Systolic Diameter (LVESD) and E/e’ ratio. Low Tricuspid Annular Plane Systolic Excursion (TAPSE) values along with high LVESD and End Diastolic Volume (EDV) values are likely to impair the gliflozin effectiveness. Conclusions Treatment with gliflozins resulted in an improvement of several echocardiographic parameters related to biventricular function and left ventricle remodeling. Several clinical parameters, as simple echocardiographic parameters, may accurately predict the cardiovascular response to gliflozins treatment.