Integrating in silico models for the prediction of mutagenicity (Ames test) of botanical ingredients of cosmetics

New Benzothiazole-Monoterpenoid Hybrids as Multifunctional Molecules with Potential Applications in Cosmetics

The Thymus vulgaris and Origanum vulgare essential oils (contained thymol and carvacrol in a range of 35-80%) are used in various products in the fields of medicine, cosmetics, and foods. Molecular hybridization between benzothiazole (BT) and phenolic monoterpenoids is a promising method for the development of biologically active compounds. New benzothiazole-monoterpenoid hybrids were synthesized through a regioselective α-amidoalkylation reaction of thymol and carvacrol with high yields (70-96%). This approach is both simple and cost-effective, employing easily accessible and inexpensive reagents to produce target molecules. The structure of the synthesized compounds was characterized spectrally using 1H-, 13C-NMR, FT-IR, and HRMS data. The newly obtained compounds are structural analogues of the UVB filter PBSA, which is used in cosmetics. The spectral properties of the aromatic products thymol hybrid (2-(4-hydroxy-5-isopropyl-2-methylphenyl)benzo[d]thiazole) and carvacrol hybrid (2-(4-hydroxy-2-isopropyl-5-methylphenyl)benzo[d]thiazole) were successfully examined, using a validated spectrophotometric method. SPF values varied from 31 to 36, compared to the PBSA (30), and were observed at concentrations of 1-0.25 mM. 2-Hydroxyphenylbenzothiazoles are known antimicrobial and antioxidant agents that have potential applications in the food industry and cosmetics as preservatives and antioxidants. In this context, antimicrobial activity of the hybrid compounds was evaluated using the agar diffusion method against E. coli, S. aureus, P. aeruginosa, and C. albicans. Compounds of methyl-2-(4-hydroxy-2-isopropyl-5-methylphenyl)benzo[d]thiazole-3(2H)-carboxylate containing carvacrol fragments showed high activity against Staphylococcus aureus ATCC 25923 (with 0.044 μmol content). The radical scavenging activity was determined using ABTS and DPPH assays, the highest activity was exhibited by the thymol hybrids ethyl-2-(4-hydroxy-5-isopropyl-2-methylphenyl)benzo[d]thiazole-3(2H)-carboxylate (IC50-133.70 ± 10 µM) and methyl-2-(4-hydroxy-5-isopropyl-2-methylphenyl)benzo[d]thiazole-3(2H)-carboxylate (IC50-157.50 ± 10 µM), defined by ABTS. The aromatic benzothiazole-monoterpenoid hybrids are classified using in silico analyses as non-mutagenic, with low toxicity, and they are non-irritating to the skin. These compounds were identified as new hit scaffolds for multifunctional molecules in cosmetics.

Molecular docking and in silico analysis of the pharmacokinetics, toxicological profile and differential gene expression of bioactive compounds from Cyrtopodium glutiniferum

The genus Cyrtopodium, from the Orchidaceae family, is widely used for its therapeutic properties in the treatment of tuberculosis, abscesses, urinary infection, and colds. C. glutiniferum, one of the species of this genus, is endemic in Brazil and largely used in herbal medicine. Thus, it is of great interest to recognize its composition, the properties of the molecules found in it. This study aimed to perform the in silico analysis of the main compounds from C. glutiniferum, on the platforms pKCSM, SwissADME, LAZAR, CLC-pred, ToxTree, DIGEPred, STRING, and Cytoscape. Further than this, the molecular docking was performed. The compounds present in the aqueous extract of C. glutiniferum were identified by UHPLC-MS/MS, finding Arbutin, Caffeic acid 4-O-glucoside, and Dihydroformononetin as the three most abundant molecules. The evaluation of the gastrointestinal absorption of Dihydroformononetin is given as high, also managing to cross the blood-brain barrier, while Arbutin can only be absorbed by the gastrointestinal tract and Caffeic acid 4-O-glucoside had very low absorption. Further analysis showed that Arbutin and Dihydroformononetin are possible leading molecules for drug synthesis, according to the prediction. Toxicological aspects were analysed, and no adverse effects were noted, but there were divergences in the mutagenic prediction of Arbutin and Dihydroformononetin, having different results in the used platforms, demonstrating that a cautious analysis and data insertion is needed in these tools to optimize them. The analysis of the differentially expressed genes predicted that the compounds can regulate several genes, including some genes associated with carcinogenesis and inflammation. The Molecular docking analysis showed high binding affinities of the molecules with different proteins. Therefore, C. glutiniferum demonstrates the potential to be used as a phytotherapeutic. The same was given through the in silico analysis of the three compounds found in the orchid, that show good individual potential.

Identification and simultaneous quantification of potential genotoxic impurities in first-line HIV drug dolutegravir sodium using fast ultrasonication-assisted extraction method coupled with GC-MS and in-silico toxicity assessment

Dolutegravir (DLG) has become a distinctive first-line antiretroviral therapy for the treatment of HIV in most countries due to its affordability, high efficacy, and low drug-drug interactions. However, the evaluation of genotoxic impurities (GTIs) in DLG and their toxicity assessment has not been explored thoroughly. Thus, in this study, a simple, fast, and selective analytical methodology was developed for the identification and determination of 7 GTIs in the comprehensive, explicit route of synthesis for the dolutegravir sodium (DLG-Na) drug. A facile, fast ultrasonication-assisted liquid-liquid extraction procedure was adapted to isolate the GTIs in DLG-Na and then analyzed using the gas chromatography (GC)-electron impact (EI)/mass spectrometer (MS) quantification (using selective ion monitoring mode) technique. This EI-GC/MS method was validated as per the current requirements of ICH Q2 (R1) guidelines. Under optimal method conditions, excellent linearities were achieved with R between 0.9959 and 0.9995, and high sensitivity was obtained in terms of detection limits (LOD) between 0.15 to 0.63 µg/g, and quantification limits (LOQ) between 0.45 to 1.66 µg/g for the seven GTIs in DLG. The obtained recoveries ranged from 98.2 to 104.3 % at LOQ, 15 µg/g, and 18 µg/g concentration levels (maximum daily dose of 100 mg). This developed and validated method is rapid, easy to adopt, specific, sensitive, and accurate in estimating the seven GTIs in a relatively complex sodium matrix of the DLG-Na drug moiety. As a method application, two different manufactured samples of DLG-Na drug substances were analyzed for the fate of the GTIs and drug safety for the intended dosage applications. Moreover, an in-silico QSAR toxicity prediction assessment was carried out to prove scientifically the potential GTI nature of each impurity from the alerting functional groups.

Mutagenic potential and structural alerts of phytotoxins

Toxic plant-produced chemicals, so-called phytotoxins, constitute a category of natural compounds belonging to a diversity of chemical classes. Some of them (e.g., alkaloids, terpenes, saponins) are associated with high toxic potency, while for many of others no toxicological data is available. In this study, the mutagenic potential of 1586 phytotoxins, as obtained from a publicly available database, was investigated applying different in silico approaches. (Q)SAR models (including statistical-based and rule-based systems) were used for the prediction of bacterial in vitro mutagenicity (Ames test) and the results from multiple tools were combined to assign consensus predicted values (i.e., positive, negative, inconclusive). The overall consensus outcome was then employed to investigate relationships between structural features of classes of phytotoxins and potential mutagenicity, allowing the identification of structural alerts raising a specific concern. The results highlighted that about 10% of the screened compounds were predicted to have mutagenic potential and the critical classes of concern, such as alkaloids, were further investigated in terms of subclasses (e.g., indole alkaloids, isoquinoline alkaloids), getting a deeper insight into the mutagenic potential of possible naturally occurring chemicals in plant materials and their structural alerts.

Virtual Extensive Read-Across: A New Open-Access Software for Chemical Read-Across and Its Application to the Carcinogenicity Assessment of Botanicals

Read-across applies the principle of similarity to identify the most similar substances to represent a given target substance in data-poor situations. However, differences between the target and the source substances exist. The present study aims to screen and assess the effect of the key components in a molecule which may escape the evaluation for read-across based only on the most similar substance(s) using a new open-access software: Virtual Extensive Read-Across (VERA). VERA provides a means to assess similarity between chemicals using structural alerts specific to the property, pre-defined molecular groups and structural similarity. The software finds the most similar compounds with a certain feature, e.g., structural alerts and molecular groups, and provides clusters of similar substances while comparing these similar substances within different clusters. Carcinogenicity is a complex endpoint with several mechanisms, requiring resource intensive experimental bioassays and a large number of animals; as such, the use of read-across as part of new approach methodologies would support carcinogenicity assessment. To test the VERA software, carcinogenicity was selected as the endpoint of interest for a range of botanicals. VERA correctly labelled 70% of the botanicals, indicating the most similar substances and the main features associated with carcinogenicity.

In vivo acute toxicity and mutagenic analysis of crude saponins from Chenopodium quinoa Willd husks

Background: As a functional food factor, quinoa saponins are valuable as additives and in medical care, pharmaceutical development, cosmetics and other fields. However, few studies have investigated the toxicity of saponins. The main purpose of this study was to evaluate the toxicity of crude saponins extracted from quinoa husks. Thus, acute toxicity and excretion experiments were carried out in rats. The Ames test, micronucleus test and mouse sperm aberration test were carried out in mice. Results: In the acute toxicity study, the obtained LD₅₀ was more than 10 g per kg per bw for both sexes, the food intake of all rats decreased over a period of time, and some rats developed diarrhea. In the case of large-dose gavage, the saponin excretion time in rats was approximately four days. When the dosage was 10 mg kg⁻¹, quinoa saponins were hydrolyzed into aglycone within 24 hours and excreted out of the body. The results of the mutagenicity experiment showed that saponins had no mutagenicity in mice. Conclusion: This work has demonstrated that quinoa saponins have limited acute toxicity effects, which provides a theoretical basis for their rational utilization.

As a functional food factor, quinoa saponins are valuable as additives and in medical care, pharmaceutical development, cosmetics and other fields.

Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA's databases

Over the past years, the European Food Safety Authority (EFSA) released to the public domain several databases, with the main objectives of collecting and storing hazard data on the substances considered in EFSA's risk assessment and secondly to serve as a basis for further development of in silico tools such as quantitative structure-activity relationship (QSAR) models. In this work, we evaluated the ability of freely available QSAR models to estimate genotoxicity and carcinogenicity properties and their possible use for screening purposes on three different EFSA's databases. With an accuracy close to 90%, the results showed good capabilities of QSAR models to predict genotoxicity in terms of bacterial reverse mutation test, while statistics for in vivo micronucleus test are not satisfactory (accuracy in the predictions close to 50%). Interestingly, results on the carcinogenicity assessment showed an accuracy in prediction close to 70% for the best models. In addition, an example of the potential application of in silico models is presented in order to provide a preliminary screening of genotoxicity properties of botanicals intended for use as food supplements.

Could deep learning in neural networks improve the QSAR models?

Assessing chemical toxicity is a multidisciplinary process, traditionally involving in vivo, in vitro and in silico tests. Currently, toxicological goal is to reduce new tests on chemicals, exploiting all information yet available. Recent advancements in machine learning and deep neural networks allow computers to automatically mine patterns and learn from data. This technology, applied to (Q)SAR model development, leads to discover by learning the structural-chemical-biological relationships and the emergent properties. Starting from Toxception, a deep neural network predicting activity from the chemical graph image, we designed SmilesNet, a recurrent neural network taking SMILES as the only input. We then integrated the two networks into C-Tox network to make the final classification. Results of our networks, trained on a ~20K molecule dataset with Ames test experimental values, match or even outperform the current state of the art. We also extract knowledge from the networks and compare it with the available mutagenic structural alerts. The advantage over traditional QSAR modelling is that our models automatically extract the features without using descriptors. Nevertheless, the model is successful if large numbers of examples are provided and computation is more complex than in classical methods.