Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Accounting for data variability, a key factor in in vivo/in vitro relationships: application to the skin sensitization potency (in vivo LLNA versus in vitro DPRA) example.

When searching for alternative methods to animal testing, confidently rescaling an in vitro result to the corresponding in vivo classification is still a challenging problem. Although one of the most important factors affecting good correlation is sample characteristics, they are very rarely integrated into correlation studies. Usually, in these studies, it is implicitly assumed that both compared values are error-free numbers, which they are not. In this work, we propose a general methodology to analyze and integrate data variability and thus confidence estimation when rescaling from one test to another. The methodology is demonstrated through the case study of rescaling the in vitro Direct Peptide Reactivity Assay (DPRA) reactivity to the in vivo Local Lymph Node Assay (LLNA) skin sensitization potency classifications. In a first step, a comprehensive statistical analysis evaluating the reliability and variability of LLNA and DPRA as such was done. These results allowed us to link the concept of gray zones and confidence probability, which in turn represents a new perspective for a more precise knowledge of the classification of chemicals within their in vivo OR in vitro test. Next, the novelty and practical value of our methodology introducing variability into the threshold optimization between the in vitro AND in vivo test resides in the fact that it attributes a confidence probability to the predicted classification. The methodology, classification and screening approach presented in this study are not restricted to skin sensitization only. They could be helpful also for fate, toxicity and health hazard assessment where plenty of in vitro and in chemico assays and/or QSARs models are available. Copyright © 2016 John Wiley & Sons, Ltd.

Photorespiratory and respiratory decarboxylations in leaves of C3 plants under different CO2 concentrations and irradiances.

We used an advanced radiogasometric method to study the effects of short-term changes in CO2 concentration ([CO2]) on the rates and substrates of photorespiratory and respiratory decarboxylations under steady-state photosynthesis and in the dark. Experiments were carried out on Plantago lanceolata, Poa trivialis, Secale cereale, Triticum aestivum, Helianthus annuus and Arabidopsis thaliana plants. Rates of photorespiration and respiration measured at a low [CO2] (40 micromol mol(-1)) were equal to those at normal [CO2] (360 micromol mol(-1)). Under low [CO2], the substrates of decarboxylation reactions were derived mainly from stored photosynthates, while under normal [CO2] primary photosynthates were preferentially consumed. An increase in [CO2] from 320 to 2300 micromol mol(-1) brought about a fourfold decrease in the rate of photorespiration with a concomitant 50% increase in the rate of respiration in the light. Respiration in the dark did not depend on [CO2] up to 30 mmol mol(-1). A positive correlation was found between the rate of respiration in the dark and the rate of photosynthesis during the preceding light period. The respiratory decarboxylation of stored photosynthates was suppressed by light. The extent of light inhibition decreased with increasing [CO2]; no inhibition was detected at 30 mmol mol(-1) CO2.