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Human Pluripotent Stem Cell Based Developmental Toxicity Assays for Chemical Safety Screening and Systems Biology Data Generation
Published on: June 17, 2015
TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity
Fabrizio Mastrolorito1, Maria Vittoria Togo1, Nicola Gambacorta1
1Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
Abstract:
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.
Insights
A new platform, TISBE, enhances developmental toxicity (Dev Tox) prediction using explainable AI and a larger dataset. It identifies toxic molecular fragments, aiding prenatal and neonatal health protection with improved accuracy.
Area of Science:
- Computational toxicology
- cheminformatics
- predictive toxicology
Background:
- Developmental toxicity (Dev Tox) is crucial for prenatal and neonatal health but poorly understood at the molecular level.
- Limited high-quality data and a lack of robust non-testing methods hinder Dev Tox research.
- Dev Tox is a significant area of animal testing in regulatory toxicology, necessitating alternative methods.
Purpose of the Study:
- To introduce TISBE (TIRESIA Improved on Structure-Based Explainability), a novel web platform for in silico developmental toxicity analysis.
- To provide an explainable artificial intelligence (XAI) framework for understanding chemical toxicity.
- To offer a reliable tool for predicting developmental toxicity and identifying responsible molecular fragments.
Main Methods:
- Development of TISBE, incorporating a larger dataset (1008 chemicals), a fragment-based XAI framework, a consensus classifier (5 ML models), and a novel applicability domain (AD) method.
- Training and validation using a 5-fold cross-validation approach.
- Application of TISBE to external datasets of bioactive compounds (ChEMBL) and pediatric drugs (TEDDY).
Main Results:
- TISBE achieved a median Matthews correlation coefficient of 0.410.
- High median sensitivity (0.984) and specificity (0.274) were observed during cross-validation.
- The platform successfully identified molecular fragments associated with toxicity or safety in tested compounds.
Conclusions:
- TISBE represents a significant advancement in in silico developmental toxicity assessment, offering explainability and improved reliability.
- The platform's ability to pinpoint toxic molecular fragments aids in understanding chemical risks.
- TISBE is freely available, supporting regulatory toxicology and drug development for enhanced child safety.
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