Research Topic - Emerging Talents in Frontiers in Pharmacology: Predictive Toxicology 2022

Here we present the Frontiers in Predictive Toxicology ‘Emerging Talents in’ article collection. A series dedicated to highlighting the emerging talent of student researchers within the field of Predictive Toxicology. Across the world students are undertaking key research as part of their education, however, most of this research is not communicated to the wider audience. We recognise that this is because many student researchers find the thought of peer-review daunting. At Frontiers, peer-review is considered a collaborative process and our interactive peer-review is tailored to provide hands-on guidance and constructive feedback to researchers. Our Topic Editors are committed to foster emerging talents and want to see student researchers strive for success at publications. Conventional toxicity testing for assessment of toxic effects in humans follow the strategy of cellular screening using a variety of cell-based assays combined with subsequent animal studies. With the development of contemporary molecular techniques termed -omics, physiologically more relevant culture systems, improved image analysis and high-throughput screening, more data can be obtained from one experiment. Further, methodologies for single cell analysis have been improved. The use of alternative (cellular and animal) models enabled the identification of compounds with specific toxicological effects (e.g. endocrine disruptors) Bioinformatics (for analysis of big data) and artificial intelligence technologies (for generation of predictive models) are also important tools for the advancement of Predictive Toxicology. In current toxicity testing combined exposure of multiple toxicants is most commonly measured, while measurement of single toxicant contribution is rarer. Also, the contribution of single cells to toxicity is not elucidated. The research presented here highlights the quality and diversity of student researchers across the field of Predictive Toxicology. We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, perspective, both experimental and computational studies that cover, but are not limited to, the following themes: • Relationship between the toxicity and chemical structures • Effect of co-exposure • Identification of toxicological pathways • Deep learning techniques to process datasets from omics data • High- throughput screening and analysis • Biological models to identify specific toxicological effects