Mechanistic Target of Rapamycin Complex 1 Promotes the Expression of Genes Encoding Electron Transport Chain Proteins and Stimulates Oxidative Phosphorylation in Primary Human Trophoblast Cells by Regulating Mitochondrial Biogenesis.

Trophoblast oxidative phosphorylation provides energy for active transport and protein synthesis, which are critical placental functions influencing fetal growth and long-term health. The molecular mechanisms regulating trophoblast mitochondrial oxidative phosphorylation are largely unknown. We hypothesized that mechanistic Target of Rapamycin Complex 1 (mTORC1) is a positive regulator of key genes encoding Electron Transport Chain (ETC) proteins and stimulates oxidative phosphorylation in trophoblast and that ETC protein expression is down-regulated in placentas of infants with intrauterine growth restriction (IUGR). We silenced raptor (mTORC1 inhibition), rictor (mTORC2 inhibition) or DEPTOR (mTORC1/2 activation) in cultured term primary human trophoblast (PHT) cells. mTORC1 inhibition caused a coordinated down-regulation of 18 genes encoding ETC proteins representing all ETC complexes. Inhibition of mTORC1, but not mTORC2, decreased protein expression of ETC complexes I-IV, mitochondrial basal, ATP coupled and maximal respiration, reserve capacity and proton leak, whereas activation of mTORC1 had the opposite effects. Moreover, placental protein expression of ETC complexes was decreased and positively correlated to mTOR signaling activity in IUGR. By controlling trophoblast ATP production, mTORC1 links nutrient and

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information

In silico toxicology: An overview of toxicity databases, prediction methodologies, and expert review: chapter 9

Understanding chemical toxicity is a necessary part of the R&D and regulatory approval process across many industries (e.g. pharmaceuticals, cosmetics, and pesticides). Toxicologists have an increasingly rich set of in vivo and in vitro methods to assess hazard and risk, which are being progressively supplemented with newer in silico approaches. The advantages and disadvantages of in silico methods are described alongside in vivo and in vitro tests. This chapter reviews a series of in silico methodologies for predicting toxicity and underpinning all in silico methodologies is the necessity to access high-quality and up-to-date toxicity study data from a variety of sources. Methods for organizing toxicity data in a harmonized manner (such as ToxML) are discussed to support combining toxicology data from different sources along with a number of commonly used toxicology databases. The three most commonly used methodologies for predicting toxicity - expert alerts, QSAR models and read-across - are reviewed. These complementary approaches provide different viewpoints concerning the structural and mechanistic basis for any prediction, alongside an analysis and rationale for supporting analog data. How this information can be then assimilated within an expert review to generate a final conclusion is discussed