Dr. Kamel Mansouri is a computational chemist who obtained his Ph.D. in computational chemistry from the University of Milano Bicocca, Italy as a Marie Curie fellow (eco-itn). He is currently leading the computational chemistry efforts at the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) within the U. S. National Institute of Environmental Health Sciences (NIEHS). He is working on several projects involving QSAR modelling, cheminformatics, and computational toxicology. Dr. Mansouri is known for his international collaborations and leading consortiums of renowned scientists in the field of QSARs and computational toxicology. In 2017, he won the Lush Prize for developing in silico alternatives to animal testing for endocrine disruptors screening.

OpenTox Virtual Conference 2023

Collaborative Computational Projects for Virtual  Screening of Endocrine Disrupting Chemicals

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the actions of natural hormones by  interfering with receptor interactions and altering pathways involved in synthesis, transport, and  metabolism. The potential for EDCs to cause adverse health effects in both humans and wildlife has  spurred the development of scientific and regulatory approaches aimed at assessing their biological  activity. Addressing this pressing need involves the utilization of high-throughput screening (HTS) in vitro  methods and computational modeling. Within the framework of the Endocrine Disruptor Screening  Program (EDSP), led by the U.S. Environmental Protection Agency (EPA), two global consortia were  established to conduct virtual screening of chemicals for potential estrogenic and androgenic activities.  The first, known as the Collaborative Estrogen Receptor (ER) Activity Prediction Project (CERAPP),  generated predictions for 32,464 chemicals. The second, the Collaborative Modeling Project for  Androgen Receptor (AR) Activity (CoMPARA), expanded upon CERAPP's predictions by including  additional simulated metabolites, ultimately assessing 55,450 unique chemical structures. These  initiatives engaged modelers and computational toxicology experts from 35 international research  groups. Their methodologies ranged from quantitative structure-activity relationships (QSARs) to  docking simulations, enabling predictions regarding binding, agonism, and antagonism activities. The  models were developed based on a shared training dataset consisting of 1,746 chemicals, which had  been evaluated through ToxCast/Tox21 HTS in vitro assays (comprising 18 assays for ER and 11 for AR).  Validation of these models involved curated literature data from diverse sources, encompassing  approximately 7,000 results for ER and 11,000 results for AR. To enhance predictive accuracy and  overcome the limitations of individual approaches, consensus models were built for both CERAPP and  CoMPARA, achieving high predictive accuracy. These consensus models were further extended beyond  their original datasets via integration into the free and open-source application OPERA. This  implementation was used to screen the entire EPA DSSTox database ~1M. The predicted ER and AR  activity results are made accessible through NTP’s Integrated Chemical Environment (https://ice.ntp.niehs.nih.gov/).