TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity.

Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity.

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 http://tiresia.uniba.it.

Diagnostic value of 18F-FDG-PET to predict the tumour immune status defined by tumoural PD-L1 and CD8+tumour-infiltrating lymphocytes in oral squamous cell carcinoma.

BACKGROUND: Lately, immune checkpoint proteins, such as programmed death 1 (PD-1) and its ligand-1 (PD-L1), have garnered attention as a new target in oral squamous cell carcinoma (OSCC). Reportedly, fluoro-D-glucose (FDG)-uptake alteration by anti-PD-1 antibody treatment depicts the response in patients with lung cancer. This study aims to elucidate the correlations between tumour immune status, clinicopathological factors, 18F-FDG-uptake and cold tumour phenotypes as low PD-L1 expression/low CD8+tumour-infiltrating lymphocytes (TILs) in OSCC. METHODS: We performed immunohistochemical analysis of PD-L1, hypoxia-inducible factor 1 A (HIF-1A), glucose transporter type 1 (GLUT1), CD8, E-cadherin and Ki-67 on 59 operable OSCC samples. We assessed the correlations between these factors and preoperative 18F-FDG-uptake, clinicopathological characteristics and prognosis. RESULTS: Low expression of PD-L1 in OSCC correlated with cancer aggressiveness, poor prognosis, high 18F-FDG-uptake with HIF-1A/GLUT1 and low E-cadherin expression and low CD8. Cold tumour phenotypes as low PD-L1 tumour cells and low stromal CD8 correlated with the poor prognosis, high 18F-FDG-uptake and E-cadherin suppression. Furthermore, the high level of preoperative 18F-FDG-uptake in OSCC was an independent predictor of the cold tumour immune status. CONCLUSIONS: 18F-FDG-uptake is an independent predictor of cold tumour in OSCC. 18F-FDG-PET imaging could be a promising diagnostic tool to estimate tumour immune status.

Expression patterns of claudins in patients with triple-negative breast cancer are associated with nodal metastasis and worse outcome.

Claudins (CLDNs) are key cell adhesion molecules, which compose tight junctions (TJs), and the disruption of TJs is associated with cancer development. Here we immunohistochemically studied expression patterns of CLDNs in 222 primary invasive breast cancers including 68 triple-negative breast cancers (TNBCs), and examined their correlation with epithelial-to-mesenchymal transition (EMT)-related markers, breast cancer stem cell (BCSC) markers, and clinicopathological features including patients' clinical outcome. Tumor margins were classified as three infiltrating growth patterns (expanding, intermediate and infiltrating). For CLDN1, 3, 4, and 7, their expression rates were more frequent in TNBCs than in other subtypes (11.8% vs 0.7%, 26.5% vs 2.0%, 48.5% vs 11.1%, and 32.4% vs 8.7%, respectively; P ≤ 0.001). In 68 TNBCs, we identified high Ki67 labeling index (LI) and the combination of CLDN4 high/CLDN7 low expression as independent predictors of axillary nodal metastasis (P = 0.019; OR, 4.36; 95%CI, 1.28-14.90 and P = 0.007; OR, 5.33; 95%CI, 1.58-17.90). Moreover, the combination of CLDN1 low/CLDN7 low/E-cadherin negative as well as tumor infiltrating patterns were predictors for worse recurrence-free survival by univariate analyses in TNBCs (P = 0.005 and P = 0.011). Our analyses provide further evidence that CLDNs would be valuable prognostic markers in TNBCs.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives.

INTRODUCTION: The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED: This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION: The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Making sense of chemical space network shows signs of criticality.

Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.

Short-term anti-remodeling effects of gliflozins in diabetic patients with heart failure and reduced ejection fraction: an explainable artificial intelligence approach.

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.