Development of a new quantitative structure-activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

Comprehensive analysis of the general toxicity, genotoxicity, and carcinogenicity of 3-acetyl-2,5-dimethylfuran in male gpt delta rats

The safety of flavoring agents has been evaluated according to classification by chemical structure and using a decision tree approach. The genotoxic potential found in some flavoring agents has highlighted the importance of efficient toxicity studies. We performed a comprehensive toxicity analysis using reporter gene transgenic rats to assess the safety of 3-acetyl-2,5-dimethylfuran (ADF), a flavoring agent exhibiting genotoxic potential in silico and in vitro assays. Male F344 gpt delta rats were given 0, 30, or 300 mg/kg body weight/day ADF by gavage for 13 weeks. In serum biochemistry analyses, triglyceride, total cholesterol, phospholipid, and total protein levels and albumin/globulin ratios were significantly altered in the 30 and 300 mg/kg groups. Histopathologically, nasal cavity toxicity and hepatocellular hypertrophy were observed in the 300 mg/kg group. In the livers of 300 mg/kg group, a significant increase in gpt mutant frequencies were observed along with ADF-specific DNA adduct formation. The number and area of glutathione S-transferase placental form-positive foci were significantly increased in the same group. Thus, ADF affected nasal cavity, liver, and lipid metabolism and showed genotoxicity and possible carcinogenicity in the liver. Overall, our comprehensive toxicity study using gpt delta rats provided insights into the safety evaluation of ADF.

Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases

The incidence rate of nervous system diseases has increased in recent years. Nerve injury or neurodegenerative diseases usually cause neuronal loss and neuronal circuit damage, which seriously affect motor nerve and autonomic nervous function. Therefore, safe and effective treatment is needed. As traditional drug research becomes slower and more expensive, it is vital to enlist the help of cutting- edge technology. Virtual screening (VS) is an attractive option for the identification and development of promising new compounds with high efficiency and low cost. With the assistance of computer- aided drug design (CADD), VS is becoming more and more popular in new drug development and research. In recent years, it has become a reality to transform non-neuronal cells into functional neurons through small molecular compounds, which provides a broader application prospect than transcription factor-mediated neuronal reprogramming. This review mainly summarizes related theory and technology of VS and the drug research and development using VS technology in nervous system diseases in recent years, and focuses more on the potential application of VS technology in neuronal reprogramming, thus facilitating new drug design for both prevention and treatment of nervous system diseases.

Multitask Deep Neural Networks for Ames Mutagenicity Prediction

The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the published in silico models for predicting mutagenicity do not take into account the test results of the individual experiments conducted for each strain. Instead, such QSAR models are generally trained employing overall labels (i.e., mutagenic and nonmutagenic). Recently, neural-based models combined with multitask learning strategies have yielded interesting results in different domains, given their capabilities to model multitarget functions. In this scenario, we propose a novel neural-based QSAR model to predict mutagenicity that leverages experimental results from different strains involved in the Ames test by means of a multitask learning approach. To the best of our knowledge, the modeling strategy hereby proposed has not been applied to model Ames mutagenicity previously. The results yielded by our model surpass those obtained by single-task modeling strategies, such as models that predict the overall Ames label or ensemble models built from individual strains. For reproducibility and accessibility purposes, all source code and datasets used in our experiments are publicly available.

In vivo and in vitro mutagenicity of perillaldehyde and cinnamaldehyde

BACKGROUND

Perillaldehyde and cinnamaldehyde are natural substances found in plants that are used as flavoring ingredients. Due to the α,β-unsaturated aldehydes in their structures, these compounds are expected to be DNA reactive. Indeed, several reports have indicated that perillaldehyde and cinnamaldehyde show positive in in vitro and in vivo genotoxicity tests. However, their genotoxic potentials are currently disputed. To clarify the mutagenicity of perillaldehyde and cinnamaldehyde, we conducted in silico quantitative structure-activity relationship (QSAR) analysis, in vitro Ames tests, and in vivo transgenic rodent gene mutation (TGR) assays.

RESULTS

In Ames tests, perillaldehyde was negative and cinnamaldehyde was positive; these respective results were supported by QSAR analysis. In TGR assays, we treated Muta™ Mice with perillaldehyde and gpt-delta mice with cinnamaldehyde up to the maximum tested doses (1000 mg/kg/day). There was no increase in gene mutations in the liver, glandular stomach, or small intestine following all treatments except the positive control (N-ethyl-N-nitrosourea at 100 mg/kg/day).

CONCLUSIONS

These data clearly show no evidence of in vivo mutagenic potentials of perillaldehyde and cinnamaldehyde (administered up to 1000 mg/kg/day) in mice; however, cinnamaldehyde is mutagenic in vitro.