In silico genotoxicity and carcinogenicity prediction for food-relevant secondary plant metabolites

Caffeic acid and ferulic acid can improve toxicological damage caused by iron overload mediated by carbonic anhydrase inhibition

The iron ion is an essential element for most forms of life, however, it can damage biological systems when found in free form. Chelation therapy is very important, but it is precarious. Caffeic and ferulic acid are antioxidant compounds with many properties described in research such as anti-inflammatory, antiobesogenic, antithrombotic, vasodilator, and anti-tumor. The aim of the study was to evaluate presenting an in silico approach on the toxicity and bioavailability of caffeic and ferulic acid, subsequently, evaluating them in an iron overload model in vivo and providing a pharmacophoric model through molecular docking. The predictive in silico test did not show relevant toxicity of the compounds, therefore, the in vivo test was performed. The rats received dextran iron and the test groups received caffeic and ferulic acid orally for six weeks. Biochemical, hematological parameters, and tissue oxidative stress marker were analyzed. The experimental model showed increased serum iron levels and changes in several serum parameters such as glucose (215.8 ± 20.3 mg/dL), ALT (512.2 ± 128.7 U/L), creatine kinase (186.8 ± 30.1 U/L), and creatine kinase isoform MB (373.3 ± 69.7 U/L). Caffeic acid and, to a lessed degree, ferullic acid, attenuated the effects of iron overload on the rat serum biochemical parameters. Docking showed a pharmacophoric model where carbonic anhydrase interacted with the test molecules and caffeic acid showed less energy expenditure in this interaction. The results illustrate a new therapeutic action of phenolic compounds on iron overload. The possible interference of carbonic anhydrase in iron metabolism needs to be elucidated.

A comprehensive review on toxicological mechanisms and transformation products of tebuconazole: Insights on pesticide management

Tebuconazole has been widely applied over three decades because of its high efficiency, low toxicity, and broad spectrum, and it is still one of the most popular fungicides worldwide. Tebuconazole residues have been frequently detected in environmental samples and food, posing potential hazards for humans. Understanding the toxicity of pesticides is crucial to ensuring human and ecosystem health, but the toxic mechanisms and toxicity of tebuconazole are still unclear. Moreover, pesticides could transform into transformation products (TPs) that may be more persistent and toxic than their parents. Herein, the toxicities of tebuconazole to humans, mammals, aquatic organisms, soil animals, amphibians, soil microorganisms, birds, honeybees, and plants were summarized, and its TPs were reviewed. In addition, the toxicity of tebuconazole TPs to aquatic organisms and mammals was predicted. Tebuconazole posed potential developmental toxicity, genotoxicity, reproductive toxicity, mutagenicity, hepatotoxicity, neurotoxicity, cardiotoxicity, and nephrotoxicity, which were induced via reactive oxygen species-mediated apoptosis, metabolism and hormone perturbation, DNA damage, and transcriptional abnormalities. In addition, tebuconazole exhibited apparent endocrine-disrupting effects by modulating hormone levels and gene transcription. The toxicity of some TPs was equivalent to and higher than tebuconazole. Therefore, further investigation is necessary into the toxicological mechanisms of tebuconazole and the combined toxicity of a mixture of tebuconazole and its TPs.

An expeditive and green chemo-enzymatic route to diester sinapoyl-l-malate analogues: sustainable bioinspired and biosourced UV filters and molecular heaters

Sinapoyl malate, naturally present in plants, has proved to be an exceptional UV filter and molecular heater for plants. Although there are nowadays industrially relevant sustainable synthetic routes to sinapoyl malate, its incorporation into certain cosmetic formulations, as well as its adsorption on plant leaves, is limited by its hydrophilicity. To overcome these obstacles, it is important to find a way to effectively control the hydrophilic-lipophilic balance of sinapoyl malate to make it readily compatible with the cosmetic formulations and stick on the waxy cuticle of leaves. To this end, herein, we describe a highly regioselective chemo-enzymatic synthesis of sinapoyl malate analogues possessing fatty aliphatic chains of variable length, enabling the lipophilicity of the compounds to be modulated. The potential toxicity (i.e., mutagenicity, carcinogenicity, endocrine disruption, acute and repeated-dose toxicity), bioaccumulation, persistence and biodegradability potential of these new analogues were evaluated in silico, along with the study of their transient absorption spectroscopy, their photostability as well as their photodegradation products.

MicotoXilico: An Interactive Database to Predict Mutagenicity, Genotoxicity, and Carcinogenicity of Mycotoxins

Mycotoxins are secondary metabolites produced by certain filamentous fungi. They are common contaminants found in a wide variety of food matrices, thus representing a threat to public health, as they can be carcinogenic, mutagenic, or teratogenic, among other toxic effects. Several hundreds of mycotoxins have been reported, but only a few of them are regulated, due to the lack of data regarding their toxicity and mechanisms of action. Thus, a more comprehensive evaluation of the toxicity of mycotoxins found in foodstuffs is required. In silico toxicology approaches, such as Quantitative Structure-Activity Relationship (QSAR) models, can be used to rapidly assess chemical hazards by predicting different toxicological endpoints. In this work, for the first time, a comprehensive database containing 4360 mycotoxins classified in 170 categories was constructed. Then, specific robust QSAR models for the prediction of mutagenicity, genotoxicity, and carcinogenicity were generated, showing good accuracy, precision, sensitivity, and specificity. It must be highlighted that the developed QSAR models are compliant with the OECD regulatory criteria, and they can be used for regulatory purposes. Finally, all data were integrated into a web server that allows the exploration of the mycotoxin database and toxicity prediction. In conclusion, the developed tool is a valuable resource for scientists, industry, and regulatory agencies to screen the mutagenicity, genotoxicity, and carcinogenicity of non-regulated mycotoxins.

In silico and in vitro screening for carcinogenic potential of angiotensin-converting enzyme inhibitors and their degradation impurities

According to some clinical observations, the use of angiotensin-converting enzyme inhibitors (ACEI) may be associated with an increased risk of cancer. The aim of the present study was to screen for the potential carcinogenicity, mutagenicity and genotoxicity of these drugs using in silico methodology. Delapril, enalapril, imidapril, lisinopril, moexipril, perindopril, ramipril, trandolapril, spirapril were thereby analyzed. In parallel, the corresponding degradation impurities, the diketopiperazine (DKP) derivatives, were also investigated. (Q)SAR computer software (VEGA-GUI and Lazar), available in the public domain, was employed. The obtained predictions suggested that none of the compounds tested (from the group of ACE-Is and DKPs) was mutagenic. Moreover, none of the ACE-Is was carcinogenic. The reliability of these predictions was high to moderate. In contrast, in the DKP group, ramipril-DKP and trandolapril-DKP were found to be potentially carcinogenic, but the reliability of this prediction was low. As for the genotoxicity screening, all compounds tested (ACE-I and DKP) were predicted to be active and genotoxic, with moexipril, ramipril, spirapril, and all DKP derivatives within the highest risk group. They were prioritized for experimental verification studies to confirm or exclude their toxic activity. On the other hand, the lowest risk of carcinogenicity was assigned to imidapril and its DKP. Then, a follow-up in vitro micronucleus assay for ramipril was performed. It showed that this drug was genotoxic via aneugenic activity, but only at concentrations exceeding real-life levels. At concentrations found in human blood after standard dose, ramipril was not genotoxic in vitro. Therefore, ramipril was considered safe for human use with a standard dosing regimen. The other compounds of concern (spirapril, moexipril and all DKP derivatives) should be subjected to analogous in vitro studies. We also concluded that the adopted in silico software was applicable for ACE-I toxicity prediction.

Prioritization of mycotoxins based on mutagenicity and carcinogenicity evaluation using combined in silico QSAR methods

Mycotoxins and their metabolites are a family of compounds that contains a great diversity of both structure and biological properties. Information on their toxicity is spread within several databases and in scientific literature. Due to the number of molecules and their structure diversity, the cost and time required for hazard evaluation of each compound is unrealistic. In that purpose, new approach methodologies (NAMs) can be applied to evaluate such large set of molecules. Among them, quantitative structure-activity relationship (QSAR) in silico models could be useful to predict the mutagenic and carcinogenic properties of mycotoxins. First, a complete list of 904 mycotoxins and metabolites was built. Then, some known mycotoxins were used to determine the best QSAR tools for mutagenicity and carcinogenicity predictions. The best tool was further applied to the whole list of 904 mycotoxins. At the end, 95 mycotoxins were identified as both mutagen and carcinogen and should be prioritized for further evaluation.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structures and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

Myristicin and Elemicin: Potentially Toxic Alkenylbenzenes in Food

Alkenylbenzenes represent a group of naturally occurring substances that are synthesized as secondary metabolites in various plants, including nutmeg and basil. Many of the alkenylbenzene-containing plants are common spice plants and preparations thereof are used for flavoring purposes. However, many alkenylbenzenes are known toxicants. For example, safrole and methyleugenol were classified as genotoxic carcinogens based on extensive toxicological evidence. In contrast, reliable toxicological data, in particular regarding genotoxicity, carcinogenicity, and reproductive toxicity is missing for several other structurally closely related alkenylbenzenes, such as myristicin and elemicin. Moreover, existing data on the occurrence of these substances in various foods suffer from several limitations. Together, the existing data gaps regarding exposure and toxicity cause difficulty in evaluating health risks for humans. This review gives an overview on available occurrence data of myristicin, elemicin, and other selected alkenylbenzenes in certain foods. Moreover, the current knowledge on the toxicity of myristicin and elemicin in comparison to their structurally related and well-characterized derivatives safrole and methyleugenol, especially with respect to their genotoxic and carcinogenic potential, is discussed. Finally, this article focuses on existing data gaps regarding exposure and toxicity currently impeding the evaluation of adverse health effects potentially caused by myristicin and elemicin.

Use of (Q)SAR genotoxicity predictions and fuzzy multicriteria decision-making for priority ranking of ethoxyquin transformation products

Ethoxyquin (EQ; 6-ethoxy-2,2,4-trimethyl-1,2-dihydroquinoline) has been used as an antioxidant in feed for pets and food-producing animals, including farmed fish such as Atlantic salmon. In Europe, the authorization for use of EQ as a feed additive was suspended, due to knowledge gaps concerning the presence and toxicity of EQ transformation products (TPs). Recent analytical studies focusing on the detection of EQ TPs in farmed Atlantic salmon feed and fillets reported the detection of a total of 27 EQ TPs, comprising both known and previously not described EQ TPs. We devised and applied an in silico workflow to rank these EQ TPs according to their genotoxic potential and their occurrence data in Atlantic salmon feed and fillet. Ames genotoxicity predictions were obtained applying a suite of five (quantitative) structure-activity relationship ((Q)SAR) tools, namely VEGA, TEST, LAZAR, Derek Nexus and Sarah Nexus. (Q)SAR Ames genotoxicity predictions were aggregated using fuzzy analytic hierarchy process (fAHP) multicriteria decision-making (MCDM). A priority ranking of EQ TPs was performed based on combining both fAHP ranked (Q)SAR predictions and analytical occurrence data. The applied workflow prioritized four newly identified EQ TPs for further investigation of genotoxicity. The fAHP-based prioritization strategy described here, can easily be applied to other toxicity endpoints and groups of chemicals for priority ranking of compounds of most concern for subsequent experimental and mechanistic toxicology analyses.

Towards developing novel and sustainable molecular light-to-heat converters

Light-to-heat conversion materials generate great interest due to their widespread applications, notable exemplars being solar energy harvesting and photoprotection. Another more recently identified potential application for such materials is in molecular heaters for agriculture, whose function is to protect crops from extreme cold weather and extend both the growing season and the geographic areas capable of supporting growth, all of which could help reduce food security challenges. To address this demand, a new series of phenolic-based barbituric absorbers of ultraviolet (UV) radiation has been designed and synthesised in a sustainable manner. The photophysics of these molecules has been studied in solution using femtosecond transient electronic and vibrational absorption spectroscopies, allied with computational simulations and their potential toxicity assessed by in silico studies. Following photoexcitation to the lowest singlet excited state, these barbituric absorbers repopulate the electronic ground state with high fidelity on an ultrafast time scale (within a few picoseconds). The energy relaxation pathway includes a twisted intramolecular charge-transfer state as the system evolves out of the Franck–Condon region, internal conversion to the ground electronic state, and subsequent vibrational cooling. These barbituric absorbers display promising light-to-heat conversion capabilities, are predicted to be non-toxic, and demand further study within neighbouring application-based fields.

The synthesis and photophysical properties of phenolic barbiturics are reported. These molecules convert absorbed ultraviolet light to heat with high fidelity and may be suitable for inclusion in foliar sprays to boost crop protection and production.

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

Carcinogenicity testing plays an essential role in identifying carcinogens in environmental chemistry and drug development. However, it is a time-consuming and label-intensive process to evaluate the carcinogenic potency with conventional 2-years rodent animal studies. Thus, there is an urgent need for alternative approaches to providing reliable and robust assessments on carcinogenicity. In this study, we proposed a DeepCarc model to predict carcinogenicity for small molecules using deep learning-based model-level representations. The DeepCarc Model was developed using a data set of 692 compounds and evaluated on a test set containing 171 compounds in the National Center for Toxicological Research liver cancer database (NCTRlcdb). As a result, the proposed DeepCarc model yielded a Matthews correlation coefficient (MCC) of 0.432 for the test set, outperforming four advanced deep learning (DL) powered quantitative structure-activity relationship (QSAR) models with an average improvement rate of 37%. Furthermore, the DeepCarc model was also employed to screen the carcinogenicity potential of the compounds from both DrugBank and Tox21. Altogether, the proposed DeepCarc model could serve as an early detection tool (https://github.com/TingLi2016/DeepCarc) for carcinogenicity assessment.

In silico and in vivo protective effect of Morus nigra leaves on oxidative damage induced by iron overload

Morus nigra L. is a plant popularly known as 'amoreira preta', very used in folk medicine. Iron overload (hemochromatosis) is a clinical condition that causes damage to various tissues due to oxidative stress. Therapy to control iron overload is still unsatisfactory. The protective effect on oxidative stress induced by iron overload was verified. Phytochemical characterization was evaluated by UHPLC-MS/MS. The in silico toxicity predictions of the main phytochemicals were performed via computer simulation. To induce iron overload, the animals received iron dextran (50 mg/kg/day). The test groups received doses of 500 and 1000 mg/kg of M. nigra extract for six weeks. Body weight, organosomatic index, serum iron, hepatic markers, cytokines, interfering factors in iron metabolism, enzymatic and histopathological evaluations were analyzed. Vanillic acid, caffeic acid, 6-hydroxycoumarin, p-coumaric acid, ferulic acid, rutin, quercitrin, resveratrol, apigenin and kaempferol were identified in the extract. In addition, in silico toxic predictions showed that the main compounds presented a low probability of toxic risk. The extract of M. nigra showed to control the mediators of inflammation and to reduce iron overload in several tissues. Our findings illustrate a novel therapeutic action of M. nigra leaves on hemochromatosis caused by iron overload.

Synthesis, spectral characterization, and biological studies of 3,5-disubstituted-1,3,4-oxadiazole-2(3H)-thione derivatives

The reaction of 3,4-dichlorophenyl-1,3,4-oxadiazole-2( 3H )-thione with piperidine derivatives via Mannich reaction was used to generate eleven novel compounds in moderate to good yields. Synthesized molecules were characterized according to their structure with ¹H NMR, ¹³C NMR and FT-IR spectral foundations, which were compatible with literature informations. Antimicrobial activity and cytotoxicity studies were done by disc diffusion and NCI-60 sulphordamine B assay methods. The antimicrobial test results revealed that synthesized compounds have better activity against gram-positive species than gram-negative ones. A total analysis of the antibacterial, antifungal, and antiyeast activity revealed that newly synthesized compounds were really active against Bacillus cereus , Bacillus ehimensis, and Bacillus thuringiensis species . For cytotoxicity, among three different cancer cell lines (HCT116, MCF7, HUH7) compounds 5c, 5d, 5e, 5f, 5g, 5i, 5j and 5k were seemed especially effective on HUH7 cancer cell line via moderate to good activity. More significantly, against liver carcinoma cell line (HUH7) most of the compounds of the series ( 5c-5g and 5i-5j ) have better IC₅₀ values (IC₅₀= 18.78 µM) than 5-Florouracil (5-FU) and also compound 5d possessed 10.1 µM value, which represents good druggable cytotoxic activity. Further, the molecules were also screened for in silico chemoinformatic and toxicity data to gather the predicted bioavailibity and safety measurements.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Effects of phytochemicals on cellular signaling: reviewing their recent usage approaches

Most of the previous studies in last three decades report evidence of interactions between the different phytochemicals and the proteins involved in signal transduction pathways using in silico, in vitro, ex vivo, and in vivo analyses. However, extrapolation of these findings for clinical purposes has not been that fruitful. The efficacy of the phytochemicals in vivo studies is limited by parameters such as solubility, metabolic degradation, excretion, etc. Various approaches have now been devised to circumvent these limitations. Recently, chemical modification of the phytochemicals are demonstrated to reduce some of the limitations and improve their efficacy. Similar to traditional medicines several combinatorial phytochemical formulations have shown to be more efficient. Further, phytochemicals have been reported to be even more efficient in the form of nanoparticles. However, systematic evaluation of their efficacy, mode of action in pathway modulation, usage and associated challenges is required to be done. The present review begins with basic understanding of how signaling cascades regulate cellular response and the consequences of their dysregulation further summarizing the developments and problems associated with the dietary phytochemicals and also discuss recent approaches in strengthening these compounds in pharmacological applications. Only context relevant studies have been reviewed. Considering the limitations and scope of the article, authors do not claim inclusion of all the early and recent studies.

Prediction of toxicity of secondary metabolites

The prediction of toxicological endpoints has gained broad acceptance; it is widely applied in early stages of drug discovery as well as for impurities obtained in the production of generic or equivalent products. In this work, we describe methodologies for the prediction of toxicological endpoints compounds, with a particular focus on secondary metabolites. Case studies include toxicity prediction of natural compound databases with anti-diabetic, anti-malaria and anti-HIV properties.

Computational prediction of toxicity of small organic molecules: state-of-the-art

The field of computational prediction of various toxicity end-points has evolved over last two decades significantly. Availability of newer modelling techniques, powerful computational resources and good-quality data have made it possible to generate reliable predictions for new chemical entities, impurities, chemicals, natural products and a lot of other substances. The field is still undergoing metamorphosis to take into account molecular complexities underlying toxicity end-points such as teratogenicity, mutagenicity, carcinogenicity, etc. Expansion of the applicability domain of these predictive models into areas other than life sciences, such as environmental and materials sciences have received a great deal of attention from all walks of life, fuelling further development and growth of the field. The present chapter discusses the state-of-the-art computational prediction of toxicity end-points of small organic molecules to balance the trade-off between the molecular complexity and the quality of such predictions, without compromising their immense utility in many fields.

Finding the bad actor: Challenges in identifying toxic constituents in botanical dietary supplements

Botanical-derived dietary supplements have widespread use in the general population. The complex and variable nature of botanical ingredients and reports of adverse responses have led to concern for negative human health impacts following consumption of these products. Toxicity testing of the vast number of available products, formulations, and combinations is not feasible due to the time and resource intensive nature of comprehensive testing. Methods are needed to assess the safety of a large number of products via more efficient frameworks. Identification of toxicologically-active constituents is one approach being used, with many advantages toward product regulation. Bioassay-guided fractionation (BGF) is the leading approach used to identify biologically-active constituents. Most BGF studies with botanicals focus on identifying pharmacologically-active constituents for drug discovery or botanical efficacy research. Here, we explore BGF in a toxicological context, drawing from both efficacy and poisonous plant research. Limitations of BGF, including loss of mixture activity and bias toward abundant constituents, and recent advancements in the field (e.g., biochemometrics) are discussed from a toxicological perspective. Identification of active constituents will allow better monitoring of market products for known toxicologically-active constituents, as well as surveying human exposure, two important steps to ensuring the safety of botanical dietary supplements.

Identification and evaluation of potentially mutagenic and carcinogenic food contaminants

Heat processing of food gives rise to a plethora of chemical compounds whose toxicological effects are largely unknown. Due to a general lack of experimental toxicological data, assessing the risks associated with the consumption of these substances remains a challenge. Computer models that allow for an in silico prediction of physicochemical and toxicological characteristics, may be able to fill current data gaps and facilitate the risk assessment of toxicologically uncharacterised chemicals, their transformation products and their biological metabolites. The overall aims of the present project were for the fellow: (i) to get acquainted with the application of computational toxicological analyses tools in risk assessment based on results and experiences from previous research performed at the German Federal Institute for Risk Assessment (BfR); and (ii) to apply the newly gained skills on historic and novel data using updated and additional in silico tools. The project contributed to the continuous further education of the fellow in the use of computational toxicology tools, corroborated findings related to the safety of heat‐induced contaminants and laid the foundations for future collaborations between the fellow's home institution, the Institute of Marine Research (IMR) in Norway, and the BfR in Germany.