Serena Greco
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Serena Greco has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Alessandro Giuliani
Environment and Health Department Istituto Superiore di Sanità Rome Italy
1 shared publication - Cecilia Bossa
Environment and Health Department, Istituto Superiore di Sanitá, 00161 Rome, Italy
1 shared publication - Chiara Laura Battistelli
Environment and Health Department, Istituto Superiore di Sanitá, 00161 Rome, Italy
1 shared publication
Latest Publications
- Open Access
A Statistical Exploration of QSAR Models in Cancer Risk Assessment: A Case Study on Pesticide-Active Substances and Metabolites
Data generated using new approach methodologies (NAMs), including in silico, in vitro, and in chemico approaches, are increasingly important for the hazard identification of chemicals. Among NAMs, (quantitative) structure–activity relationship (Q)SAR models occupy a peculiar position by allowing (in principle) a toxicity estimate on the sole basis of chemical structural information, leveraging upon toxicity profiles of already tested chemicals (a training set). Consequently, the metrics adopted for the estimation of both the congruence of the test chemicals with the training set and the risk categorization are of paramount importance. This paper comprises a small-scale, mainly methodological study to investigate these aspects and assess the general coherence between the results from different (Q)SAR models applied to the assessment of the carcinogenicity of pesticide-active substances and metabolites. The results of the present study underline the significant potential of using (Q)SAR models, together with limitations, such as inconsistencies in results across models and the intrinsic constraints of their applicability domain. The critical role of a priori strategies adopted in defining the applicability domain of the models is highlighted, emphasizing the need for user-transparent definitions. This is a crucial step for a sensible integration of the information coming from different NAMs.
Open AccessA Statistical Exploration of QSAR Models in Cancer Risk Assessment: A Case Study on Pesticide-Active Substances and Metabolites
Data generated using new approach methodologies (NAMs), including in silico, in vitro, and in chemico approaches, are increasingly important for the hazard identification of chemicals. Among NAMs, (quantitative) structure–activity relationship (Q)SAR models occupy a peculiar position by allowing (in principle) a toxicity estimate on the sole basis of chemical structural information, leveraging upon toxicity profiles of already tested chemicals (a training set). Consequently, the metrics adopted for the estimation of both the congruence of the test chemicals with the training set and the risk categorization are of paramount importance. This paper comprises a small-scale, mainly methodological study to investigate these aspects and assess the general coherence between the results from different (Q)SAR models applied to the assessment of the carcinogenicity of pesticide-active substances and metabolites. The results of the present study underline the significant potential of using (Q)SAR models, together with limitations, such as inconsistencies in results across models and the intrinsic constraints of their applicability domain. The critical role of a priori strategies adopted in defining the applicability domain of the models is highlighted, emphasizing the need for user-transparent definitions. This is a crucial step for a sensible integration of the information coming from different NAMs.