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TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity.
Mastrolorito, Fabrizio; Togo, Maria Vittoria; Gambacorta, Nicola; Trisciuzzi, Daniela; Giannuzzi, Viviana; Bonifazi, Fedele; Liantonio, Antonella; Imbrici, Paola; De Luca, Annamaria; Altomare, Cosimo Damiano; Ciriaco, Fulvio; Amoroso, Nicola; Nicolotti, Orazio.
Affiliation
  • Mastrolorito F; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Togo MV; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Gambacorta N; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Trisciuzzi D; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Giannuzzi V; Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy.
  • Bonifazi F; Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy.
  • Liantonio A; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Imbrici P; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • De Luca A; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Altomare CD; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Ciriaco F; Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Amoroso N; Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
  • Nicolotti O; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy.
Chem Res Toxicol ; 37(2): 323-339, 2024 02 19.
Article in En | MEDLINE | ID: mdl-38200616
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence Type of study: Prognostic_studies / Risk_factors_studies Limits: Animals / Child / Humans / Newborn Language: En Journal: Chem Res Toxicol Journal subject: TOXICOLOGIA Year: 2024 Document type: Article Affiliation country: Italy Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence Type of study: Prognostic_studies / Risk_factors_studies Limits: Animals / Child / Humans / Newborn Language: En Journal: Chem Res Toxicol Journal subject: TOXICOLOGIA Year: 2024 Document type: Article Affiliation country: Italy Country of publication: United States