Mode of action studies with phthalate acid monoesters: pharmacokinetic and pharmacodynamic factors affecting steroidogenesis

The use of phthalate esters in plastics and resulting human exposure has led to concern over potential adverse effects in fetal development. This project provided data and quantitative tools to improve phthalate risk assessments. In vivo, in vitro and in silico experiments evaluated pharmacokinetic and pharmacodynamic factors responsible for anti-androgenic effects of phthalate esters. For pharmacokinetics, plasma and tissue metabolite levels were measured in maternal and fetal rats following DBP administration. A physiologically based pharmacokinetic (PBPK) model was developed for DBP distribution in rat gestation, tested against a variety of data across life-stages, doses and exposure routes, and accurately predicted maternal and fetal plasma MBP levels for acute and repeated dosing. The validated model permitted direct correlation of testes phthalate concentrations and testosterone. When extended to DEHP, the model also predicted MEHP kinetics. For pharmacodynamic evaluation, monoester concentrations were measured in the fetal testes after repeated doses of BBP, DEP, DBP, DEHP, and DMP. An in vitro assay tested the effect of inhibition of steroidogenesis directly in the Leydig cell. The differential ability of the monoesters to cause developmental toxicity was found to result from differences in their pharmacodynamic potency. Finally, we attempted to identify the molecular target for the phthalates in the Leydig cell. The phospholipase A2 (PLA2) inhibitor CQ and MEHP had a similar ability to inhibit testosterone production, steroidogenic gene expression and AA release in the LH-stimulated (MA-10) Leydig cell. Both compounds interfered with translocation of fluorescently tagged cPLA2 in human HEK-cells after activation by a calcium ionophore, providing at least indirect evidence that inhibition of AA release by cPLA2 is likely to be involved in phthalate anti-androgenic effects. When CQ was administered to the pregnant rat, fetal testes testosterone levels were reduced in a dose-dependent manner. CQ also down-regulated steroidogenic genes as noted with active phthalate administration. Our results strongly indicate that cPLA2 is a key target of these phthalates in relation to decreased testosterone production. The improved understanding of phthalate dose-response and mechanism of action, together with in vitro derived potencies of phthalates for testosterone inhibition, should greatly improve cumulative risk assessments for the phthalates

ESAC Opinion on the Scientific Validity of the AR-CALUX Test Method

ESAC, the EURL ECVAM Scientific Advisory Committee, advises EURL ECVAM on scientific issues. Its main role is to conduct independent peer review of validation studies of alternative test methods and to assess their scientific validity for a given purpose. The committee reviews the appropriateness of study design and management, the quality of results obtained and the plausibility of the conclusions drawn. ESAC peer reviews are formally initiated with a EURL ECVAM Request for ESAC Advice, which provides the necessary background for the peer-review and establishes its objectives, timelines and the questions to be addressed. The peer review is normally prepared by specialised ESAC Working Groups. ESAC's advice to EURL ECVAM is formally provided as 'ESAC Opinions' and 'Working Group Reports' at the end of the peer review. ESAC may also issue Opinions on other scientific issues of relevance to the work and mission of EURL ECVAM but not directly related to a specific alternative test method. The ESAC Opinion expressed in this report relates to the peer-review of the AR-CALUX in vitro test method.JRC.F.3-Chemicals Safety and Alternative Method

A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation

Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation

ESAC Opinion on the Scientific Validity of the Bioelution Test Method: ESAC Opinion No. 2019-03 of 2 December 2019

ESAC, the EURL ECVAM Scientific Advisory Committee, advises EURL ECVAM on scientific issues. Its main role is to conduct independent peer review of validation studies of alternative test methods and to assess their scientific validity for a given purpose. The committee reviews the appropriateness of study design and management, the quality of results obtained and the plausibility of the conclusions drawn. ESAC peer reviews are formally initiated with a EURL ECVAM Request for ESAC Advice, which provides the necessary background for the peer-review and establishes its objectives, timelines and the questions to be addressed. The peer review is normally prepared by specialised ESAC Working Groups. ESAC's advice to EURL ECVAM is formally provided as 'ESAC Opinions' and 'Working Group Reports' at the end of the peer review. ESAC may also issue Opinions on other scientific issues of relevance to the work and mission of EURL ECVAM but not directly related to a specific alternative test method. The ESAC Opinion expressed in this report relates to the peer-review of the Bioelution in vitro test method.JRC.F.3-Chemicals Safety and Alternative Method

An integrated approach to predict activators of NRF2 - the transcription factor for oxidative stress response

A variety of environmental and physiological conditions can cause oxidative stress that damage cellular components such as DNA, proteins and lipids. Oxidative stress is implicated in many human diseases including cancer, cardiovascular diseases, neurological diseases, inflammatory diseases, and aging. The nuclear factor erythroid 2–related factor 2 (NRF2) is a transcriptional factor that plays a key role in the cellular antioxidant defense system as it regulates transcription of antioxidant proteins and detoxifying enzymes. There is an urgent need to identify novel compounds that activate NRF2 and enhance antioxidant defense. We collected data from the high-throughput screening of NRF2 activators and identified molecular fragments (structural alerts) associated with the activation of NRF2. We also developed ten classification models using different types of molecular descriptors and machine learning techniques. Two approaches were used to establish the applicability domain of developed models: the structure-based approach and the distance to model approach. The best performing model that used message passing neural network (MPNN) technique showed accuracy of 87 % for the test set of chemicals within the distance to model of 0.3. The integrative approach using a combination of generated structural alerts and MPNN model was used to screen approved drugs collected in the DrugBank to identify potential NRF2 activators. Out of 2393 screened chemicals 138 compounds were predicted as NRF2 activators by both approaches. Analysis of these compounds showed that some drugs were already known activators of NRF2 while others are potentially novel activators

Applying a Tiered Risk-based Approach to Prioritizing Thousands of Chemicals for Further Evaluation: A Comparison of Current High Throughput Computational Approaches

Poster presentation to Society of Toxicology annual meeting March 201