Comparison of in silico models for prediction of mutagenicity

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

The human population will be approximately 9.7 billion by 2050, and food security has been identified as one of the key issues facing the global population. Agrochemicals are an important tool available to farmers that enable high crop yields and continued access to healthy foods, but the average new agrochemical active ingredient takes more than ten years, 350 million dollars, and 20,000 animals to develop and register. The time, monetary, and animal costs incentivize the use of New Approach Methodologies (NAMs) in early-stage screening to prioritize chemical candidates. This review outlines NAMs that are currently available or can be adapted for use in early-stage screening agrochemical programs. It covers new in vitro screens that are on the horizon in key areas of regulatory concern. Overall, early-stage screening with NAMs enables the prioritization of development for agrochemicals without human and environmental health concerns through a more directed, agile, and iterative development program before animal-based regulatory testing is even considered.

Computational framework for identifying and evaluating mutagenic and xenoestrogenic potential of food additives

Food additives are chemicals incorporated in food to enhance its flavor, color and prevent spoilage. Some of these are associated with substantial health hazards, including developmental disorders, increase cancer risk, and hormone disruption. Hence, this study aimed to comprehend the in-silico toxicology framework for evaluating mutagenic and xenoestrogenic potential of food additives and their association with breast cancer. A total of 2885 food additives were screened for toxicity based on Threshold of Toxicological Concern (TTC), mutagenicity endpoint prediction, and mutagenic structural alerts/toxicophores identification. Ten food additives were identified as having mutagenic potential based on toxicity screening. Furthermore, Protein-Protein Interaction (PPI) analysis identified ESR1, as a key hub gene in breast cancer. KEGG pathway analysis verified that ESR1 plays a significant role in breast cancer pathogenesis. Additionally, competitive interaction studies of the predicted potential mutagenic food additives with the estrogen receptor-α were evaluated at agonist and antagonist binding sites. Indole, Dichloromethane, Trichloroethylene, Quinoline, 6-methyl quinoline, Ethyl nitrite, and 4-methyl quinoline could act as agonists, and Paraldehyde, Azodicarbonamide, and 2-acetylfuranmay as antagonists. The systematic risk assessment framework reported in this study enables the exploration of mutagenic and xenoestrogenic potential associated with food additives for hazard identification and management.

Identification of New Hepatic Metabolites of Miconazole by Biological and Electrochemical Methods Using Ultra-High-Performance Liquid Chromatography Combined with High-Resolution Mass Spectrometry

The main objective of this study was to investigate the metabolism of miconazole, an azole antifungal drug. Miconazole was subjected to incubation with human liver microsomes (HLM) to mimic phase I metabolism reactions for the first time. Employing a combination of an HLM assay and UHPLC-HRMS analysis enabled the identification of seven metabolites of miconazole, undescribed so far. Throughout the incubation with HLM, miconazole underwent biotransformation reactions including hydroxylation of the benzene ring and oxidation of the imidazole moiety, along with its subsequent degradation. Additionally, based on the obtained results, screen-printed electrodes (SPEs) were optimized to simulate the same biotransformation reactions, by the use of a simple, fast, and cheap electrochemical method. The potential toxicity of the identified metabolites was assessed using various in silico models.

Strategy proposal using QSAR models to approach mutagenicity assessment of non intentionally added substances in recycled plastic resins

CONTEXT

Transition to the use of recycled plastics raises an issue concerning safety assessment of Non Intentionally Added Substances (NIAS). To assess the mutagenic potential of the recycled polyethylene impurities and to evaluate the need to perform in vitro assays on recycled resins, this study lies in identifying existing NIAS associated with recycled Low/High Density Polyethylene and assessing the mutagenicity data-gaps by employing in silico tools.

METHODS

Quantitative Structure-Activity Relationship (QSAR) models predicting Ames mutagenicity were selected from literature, then NIAS were run to 1/evaluate performances of each model, 2/apply a QSAR strategy on the NIAS molecular space and address data-gaps.

RESULTS

Among the 165 NIAS identified, experimental Ames results were not found for 50 substances while the substances with experimental data were predominantly negatives. No individual model was able to predict all NIAS due to applicability domain limitations. Taking into account 1/calculated performances, 2/availability of applicability domain, 3/description of the Training Set, an Integrated Strategy was founded including Sarpy, Consensus and Protox to extend the applicability domain.

CONCLUSION & PERSPECTIVES

Existing data and predictions generated by this strategy suggest a low mutagenic potential of NIAS. Further investigation is needed to explore other genotoxicity mechanisms.

A deep-learning approach for identifying prospective chemical hazards

With the aim of helping to set safe exposure limits for the general population, various techniques have been implemented to conduct risk assessments for chemicals and other environmental stressors; however, none of these tools facilitate the identification of completely new chemicals that are likely hazardous and elicit an adverse biological effect. Here, we detail a novel in silico, deep-learning framework that is designed to systematically generate structures for new chemical compounds that are predicted to be chemical hazards. To assess the utility of the framework, we applied the tool to four endpoints related to environmental toxicants and their impacts on human and animal health: (i) toxicity to honeybees, (ii) immunotoxicity, (iii) endocrine disruption via ER-α antagonism, and (iv) mutagenicity. In addition, we characterized the predicted potency of these compounds and examined their structural relationship to existing chemicals of concern. As part of the array of emerging new approach methodologies (NAMs), we anticipate that such a framework will be a significant asset to risk assessors and other environmental scientists when planning and forecasting. Though not in the scope of the present study, we expect that the methodology detailed here could also be useful in the de novo design of more environmentally-friendly industrial chemicals.

Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems

Mutagenicity is considered an important endpoint from the regulatory, environmental and medical points of view. Due to the wide number of compounds that may be of concern and the enormous expenses (in terms of time, money, and animals) associated with rodent mutagenicity bioassays, this endpoint is a major target for the development of alternative approaches for screening and prediction. The majority of old-aged expert systems and quantitative structure-activity relationship (QSAR) models may show reduced performance over time for their application on newer chemical candidates; thus, researchers constantly try to improve the modeling strategies. In our report, we initially performed traditional classification-based linear discriminant analysis (LDA) QSAR modeling using the benchmark Ames dataset of diverse chemicals (6512 compounds) to recognize the relationship between the molecules and their potential mutagenic behavior. The classical LDA QSAR model is developed from a selected set of 2D descriptors. The LDA QSAR model was developed by using a total of 31 descriptors identified from the analysis of the most discriminating features. Additionally, we have used similarity-derived features obtained from the read-across (RA) to develop an RA-based QSAR model. The developed RA-based LDA QSAR model has better predictivity, transferability, and interpretability compared to the LDA QSAR model, and it uses a very small number of descriptors compared to the classical QSAR model. Different machine learning (ML) models were also developed using the descriptors appearing in the read-across-based LDA QSAR model for comparative studies. We have checked the prediction quality of 216 true external set compounds using the novel similarity-derived RA model. The performance of the OECD toolbox is also compared with the RA-derived LDA QSAR model for a true external set. The current study aimed to explore the significance of the read-across-based algorithm and its application to the most current experimental mutagenicity data to complement already available expert systems.

Electrochemical Simulation of Phase I Hepatic Metabolism of Voriconazole Using a Screen-Printed Iron(II) Phthalocyanine Electrode

Understanding the metabolism of pharmaceutical compounds is a fundamental prerequisite for ensuring their safety and efficacy in clinical use. However, conventional methods for monitoring drug metabolism often come with the drawbacks of being time-consuming and costly. In an ongoing quest for innovative approaches, the application of electrochemistry in metabolism studies has gained prominence as a promising approach for the synthesis and analysis of drug transformation products. In this study, we investigated the hepatic metabolism of voriconazole, an antifungal medication, by utilizing human liver microsomes (HLM) assay coupled with LC-MS. Based on the obtained results, the electrochemical parameters were optimized to simulate the biotransformation reactions. Among the various electrodes tested, the chemometric analysis revealed that the iron(II) phthalocyanine electrode was the most effective in catalyzing the formation of all hepatic voriconazole metabolites. These findings exemplify the potential of phthalocyanine electrodes as an efficient and cost-effective tool for simulating the intricate metabolic processes involved in drug biotransformation, offering new possibilities in the field of pharmaceutical research. Additionally, in silico analysis showed that two detected metabolites may exhibit significantly higher acute toxicity and mutagenic potential than the parent compound.

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking

Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood-brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.

Assessing chemical hazard and unraveling binding affinity of priority pollutants to lignin modifying enzymes for environmental remediation

Lignin-modifying enzymes (LMEs) are impactful biocatalysts in environmental remediation applications. However, LMEs-assisted experimental degradation neglects the molecular basis of pollutant degradation. Furthermore, throughout the remediation process, the inherent hazards of environmental pollutants remain untapped for in-depth toxicological endpoints. In this investigation, a predictive toxicological framework and a computational framework adopting LMEs were employed to assess the hazards of Priority Pollutants (PP) and its possible LMEs-assisted catalytic screening. The potential hazardous outcomes of PP were assessed using Quantitative structure-activity relationship (QSARs)-based techniques including Toxtree, ECOSAR, and T.E.S.T. tools. Toxicological findings revealed positive outcomes in a multitude of endpoints for all PP. The PP compound 2,3,7,8-TCDD (dioxin) was found to exhibit the lowest concentration of aquatic toxicity implementing aquatic model systems; LC₅₀ as 0.01, 0.01, 0.04 (mg L^-1) for Fish (96 H), Daphnid (48 H), Green algae (96 H) respectively. T.E.S.T. results revealed that chloroform, and 2-chlorophenol both seem to be developmental toxicants. Subsequently, LMEs-assisted docking procedure was employed in predictive mitigation of PP. The docking approach as predicted degradation revealed the far lowest docking energy score for Versatile peroxidase (VP)- 2,3,7,8-TCDD docked complex with a binding energy of -9.2 (kcal mol^-1), involved PHE-46, PRO-139, PRO-141, ILE-148, LEU-165, HIS-169, LEU-228, MET-262, and MET-265 as key interacting amino acid residues. Second most ranked but lesser than VP, Lignin peroxidase (LiP)- 2,3,7,8-TCDD docked complex exhibited a rather lower binding affinity score (-8.8 kcal mol^-1). Predictive degradation screening employing comparative docking revealed varying binding affinities, portraying that each LMEs member has independent feasibility to bind PP as substrate. Predictive findings endorsed the hazardous nature of associated PP in a multitude of endpoints, which could be attenuated by undertaking LMEs as a predictive approach to protect the environment and implement it in regulatory considerations.

Toxicological assessment of SGLT2 inhibitors metabolites using in silico approach

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are the latest class of drugs approved to treat type 2 DM (T2DM). Although adverse effects are often caused by a metabolite rather than the drug itself, only the safety assessment of disproportionate drug metabolites is usually performed, which is of particular concern for drugs of chronic use, such as SGLT2i. Bearing this in mind, in silico tools are efficient strategies to reveal the risk assessment of metabolites, being endorsed by many regulatory agencies. Thereby, the goal of this study was to apply in silico methods to provide the metabolites toxicity assessment of the SGLT2i. Toxicological assessment from SGLT2i metabolites retrieved from the literature was estimated using the structure and/or statistical-based alert implemented in DataWarrior and ADMET predictorTM softwares. The drugs and their metabolites displayed no mutagenic, tumorigenic or cardiotoxic risks. Still, M1-2 and M3-1 were recognized as potential hepatotoxic compounds and M1-2, M1-3, M3-1, M3-2, M3-3 and M4-3, were estimated to have very toxic LD50 values in rats. All SGLT2i and the metabolites M3-4, M4-1 and M4-2, were predicted to have reproductive toxicity. These results support the awareness that metabolites may be potential mediators of drug-induced toxicities of the therapeutic agents.

A predictive toolset for the identification of degradation pattern and toxic hazard estimation of multimeric hazardous compounds persists in water bodies

An array of industrial processing units generates many multimeric hazardous compounds, such as complex technical lignin and its toxic derivatives, thereby persist in expelled water bodies. The inclusion of some group of motifs in the complex technical lignin structure helps it resist degrade biologically, most often even recalcitrant. Relatively small concentrations of lignin are harmful to aquatic organisms and can trigger environmental hazards. Sadly, the entire biotransformation pathway and insightful information about these toxic derivatives are incomplete and missing in the literature. This is mainly because the current conventional treatments often fail to identify all transformed compounds and their environmental fate. Thus, a robust toolset is much needed to cover this literature gap. Inadequate performance of conventional remediation processes and biological degradation patterns can be maximally optimized with the aid of predictive toolset methods that could offer better degradability and complete transformed compound information. A predictive toolset-assisted biodegradation pattern determination is a multifaceted and reliable analytical technique that can help to overcome existing shortcomings by providing an entire transformation pathway. Considering the above critiques, this work reports on the degradation pattern, and toxicological endpoints of five hazardous compounds, i.e., 2-chlorosyringaldehyde, 5-chlorovanillin, catechol, guaiacyl 4-O-5 guaiacyl, and syringyl β-O-4 syringyl β-O-4 sinapyl alcohol, that persists in water matrices. The predictive transformation pattern was revealed notably less complex end-products of catechol as; succinate, and 2-Oxo-4-pentenoate. The gastrointestinal (GI) absorption rate was found high for all tested compounds, excluding trimer compound, i.e., syringyl β-O-4 syringyl β-O-4 sinapyl alcohol. The toxicity and persistence profile tested via Toxtree showed that the Cramer Rules, Verhaar Scheme, and Structural Alerts for Reactivity, (START) biodegradation ability as positive, and all five target compounds were found as class-II persistent compounds. Furthermore, the Ecological Structure-Activity Relationships (ECOSAR)assisted testing specifies that all tested derivatives have multiple aquatic toxic levels. In summary, the current findings endorse the hazardous compounds and undertake prescreening of the deprivation policy to protect the environment.

The VEGAHUB Platform: The Philosophy and the Tools

VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.

In silico analytical toolset for predictive degradation and toxicity of hazardous pollutants in water sources

Different phenolic compounds, including multimeric lignin derivatives in the β-O-4 form, are among the most prevalent compounds in wastewater, often generated from paper industries. Relatively small concentrations of lignin are hazardous to aquatic organisms and can trigger severe environmental hazards. Herein, we present a predictive toolset to insight the induced toxic hazards prediction, and their Lignin peroxidase (LiP)-assisted degradation mechanism of selected multimeric lignin model compounds. T.E.ST and Toxtree toolset were deployed for toxic hazards estimation in different endpoints. To minimize the concerning hazards, we screened multimeric compounds for binding affinity with LiP. The binding affinity was found to be significantly lower than the reference compound. An Extra precision (XP) Glide score of -6.796 kcal/mol was found for dimer (guaiacyl 4-O-5 guaiacyl) complex as lowest compared to reference compound (-4.007 kcal/mol). The active site residues ASP-153, HIP-226, VAL-227, ARG-244, GLU-215, 239, PHE-261 were identified as site-specific key binding AA residues actively involved with corresponding ligands, forming Hydrophobic, H-Bond, π-Stacking, π-π type interactions. The DESMOND-assisted molecular dynamics simulation's (MDS) trajectories of protein-ligand revealed the considerable binding behavior and attained stability and system equilibrium state. Such theoretical and predictive conclusions indicted the feasibility of LiP assisted sustainable mitigation of lignin-based compounds, and such could be used to protect the environment from the potential hazards posed by recognized similar pollutants.

Identification of Natural Products as SENP2 Inhibitors for Targeted Therapy in Heart Failure

Aims: Sentrin-specific protease -2 (SENP2) is involved in deSUMOylation. Increased deSUMOylation in murine hearts by SENP2 upregulation resulted in cardiac dysfunction and congenital heart defects. Natural compounds via regulating cell proliferation and survival, induce cell cycle cessation, cell death, apoptosis, and producing reactive oxygen species and various enzyme systems cause disease prevention. Then, natural compounds can be suitable inhibitors and since SENP2 is a protein involved in heart disease, so our aim was inhibition of SENP2 by natural products for heart disease treatment. Material and methods: Molecular docking and molecular dynamics simulation of natural products i.e. Gallic acid (GA), Caffeic acid (CA), Thymoquinone (TQ), Betanin, Betanidin, Fisetin, and Ebselen were done to evaluate the SENP2 inhibitory effect of these natural products. The toxicity of compounds was also predicted. Results: The results showed that Betanin constituted a stable complex with SENP2 active site as it revealed low RMSD, high binding energy, and hydrogen bonds. Further, as compared to Ebselen, Betanin demonstrated low toxicity, formed a stable complex with SENP2 via four to seven hydrogen bonds, and constituted more stable MD plots. Therefore, depending upon the outcomes presented herein, Betanin significantly inhibited SENP2 and hence may be considered as a suitable natural compound for the treatment of heart failure. Further clinical trials must be conducted to validate its use as a potential SENP2 inhibitor.

Comparative analysis of in vivo and in silico toxicity evaluation of the organoiodine compounds towards D.magna using multivariate chemometric approach: A study on the example of amiodarone phototransformation products

In the present study the photochemical fate of organoiodine compound - amiodarone was performed. The drug turned out to be highly susceptible to UV-Vis irradiation, especially in the presence of humic substances and organic matrix. Qualitative LC-MS analysis revealed formation of twelve - mainly previously unreported - transformation products (TPs). Four major TPs were submitted to the toxicity analysis with the use of D. magna. All of the tested TPs presented higher toxic potential than the parent compound. The phenolic TPs were approximately 100 times more toxic than amiodarone. Toxic properties of the major TPs resulted in steadily increasing toxic potential of the photo-generated mixture over the time of irradiation. Moreover, the experimental toxicity data, concerning the TPs, were compared with results estimated by 6 in silico models with the use of a multivariate chemometric analysis. The results showed that the applied computational methods were able neither to correctly predict toxic properties of the studied compounds, nor the trends in change of their toxic parameters. Additional validation of in silico models ability to predict toxicity of iodinated organic compounds showed that the studied computational methods do not present sufficient prediction ability. Therefore their estimations concerning organoiodines should be verified using experimental tests.

Computational investigation of the alkaloids of Pilocarpus microphyllus species as phytopharmaceuticals for the inhibition of sterol 14α-demethylase protease of Trypanosoma cruzi

Trypanosoma cruzi is a protozoan transmitted by the insect Triatoma infestans, popularly known as kissing bug. This protozoan causes the Chagas disease, a Neglected Tropical Disease. This study aimed to investigate, through DFT method and B3LYP hybrid functional, the physicochemical, pharmacokinetic, and pharmacodynamic properties of the alkaloids present in the leaves of the species Pilocarpus microphyllus (jaborandi) as a potential inhibitory activity on the protease sterol 14α-demethylase of T. cruzi associated with the techniques of molecular docking, molecular dynamics, MM-PBSA and ADMET predictions. The molecules of isopilosine, epiisopiloturine, epiisopilosine, and pilosine showed up the lowest binding energies by molecular docking, good human intestinal absorption, low penetration in the blood-brain barrier, antiprotozoal and anticarcinogenic activities in ADMET studies. It has been observed a better binding affinity of the sterol 14α-demethylase protease with isopilosine in molecular dynamics and MM-PBSA studies, which indicates it as a potential drug candidate for Chagas disease.Communicated by Ramaswamy H. Sarma.

Using VEGAHUB Within a Weight-of-Evidence Strategy

Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools as alternatives to animal testing. The main interest is not solely on the use of in silico tools, or in read-across, but on better toxicological safety assessment of substances, and for this purpose more advanced, integrated strategies have to be implemented. VEGAHUB wants to promote this broader view, not necessarily focused on a specific approach. Applying multiple tools and complementary approaches instead of one technique may provide more elements for a more robust evaluation, but at the same time it is important to have a conceptual scheme to integrate multiple, heterogeneous lines of evidence. We will show how the user can benefit from the diversity of tools available within the platform VEGAHUB for assessing the biological properties of chemical substances on an example of (non)mutagenicity.

Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity

The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential candidates based on their genotoxicity and carcinogenicity information to inform the selection of candidate chemicals for prioritization under TSCA. The approach was underpinned by a large database of carcinogenicity and genotoxicity information that had been compiled from various public sources. Carcinogenicity data included weight-of-evidence human carcinogenicity evaluations and animal cancer data. Genotoxicity data included bacterial gene mutation data from the Salmonella (Ames) and Escherichia coli WP2 assays and chromosomal mutation (clastogenicity) data. Additionally, Ames and clastogenicity outcomes were predicted using the alert schemes within the OECD QSAR Toolbox and the Toxicity Estimation Software Tool (TEST). The evaluation workflows for carcinogenicity and genotoxicity were developed along with associated scoring schemes to make an overall outcome determination. For this case study, two sets of chemicals, the TSCA Active Inventory non-confidential portion list available on the EPA CompTox Chemicals Dashboard (33,364 chemicals, 'TSCA Active List') and a representative proof-of-concept (POC) set of 238 chemicals were profiled through the two workflows to make determinations of carcinogenicity and genotoxicity potential. Of the 33,364 substances on the 'TSCA Active List', overall calls could be made for 20,371 substances. Here 46.67%% (9507) of substances were non-genotoxic, 0.5% (103) were scored as inconclusive, 43.93% (8949) were predicted genotoxic and 8.9% (1812) were genotoxic. Overall calls for genotoxicity could be made for 225 of the 238 POC chemicals. Of these, 40.44% (91) were non-genotoxic, 2.67% (6) were inconclusive, 6.22% (14) were predicted genotoxic, and 50.67% (114) genotoxic. The approach shows promise as a means to identify potential candidates for prioritization from a genotoxicity and carcinogenicity perspective.

Photochemical transformation of fentanyl under the simulated solar radiation - Enhancement of the process by heterogeneous photocatalysis and in silico analysis of toxicity

In this study the photochemical transformation of fentanyl-a very potent opioid drug-under simulated solar radiation was investigated for the first time. This pharmaceutical is frequently detected in various environment samples at concentrations that should be regarded as potentially harmful. Nevertheless, to date, no drug phototransformation products (TPs) have been reported. Considering fentanyl's exceptionally high toxicity, knowledge of the properties of these potential TPs is essential in order to properly assess its pollution impact. In this study, all photolytic experiments were performed using a xenon lamp (D65 filter) and RP-UHPLC coupled with the ESI-high-resolution tandem mass spectrometry. The phototransformation of fentanyl in natural river water and the application of heterogeneous photocatalysis as a possible way of decontaminating water were also investigated. Fentanyl turned out to be photostable, but twenty-six previously unreported TPs (formed mainly as a consequence of hydroxylation and oxidation) were found and characterized. The applied catalysts-TiO₂ and ZnO-showed very high effectiveness, and the presence of the natural water matrix further increased the photodecomposition rate (up to 600 times) relative to direct photolysis. Importantly, the almost complete degradation of the parent compound as well as its TPs after 16 min of irradiation indicated that heterogeneous photocatalysis can be considered an efficient way of treatment of fentanyl-contaminated water. The computational analysis of toxicity showed that fentanyl may be more harmful to rodents and aquatic species than its TPs. However, some of these products are probably more mutagenic and developmentally toxic. Additionally, one product in particular may be a strong estrogenic compound, proving the importance of assessing TPs' toxic properties. The evaluation of bioaccumulation, bioconcentration and biodegradability revealed that fentanyl possesses unfavorable properties compared to TPs.

A New Symmetrical Thiazolidinedione Derivative: In Silico Design, Synthesis, and In Vivo Evaluation on a Streptozotocin-Induced Rat Model of Diabetes

By activating PPAR-γ, thiazolidinediones normalize glucose levels in animal models of type 2 diabetes and in patients with this pathology. The aim of the present study was to analyze 219 new derivatives in silico and select the best for synthesis, to be evaluated for acute oral toxicity in female rats and for control of diabetes-related parameters in a rat model of streptozotocin-induced diabetes. The best compound was chosen based on pharmacokinetic, pharmacodynamic, and toxicological parameters obtained in silico and binding orientation observed by docking simulations on PPAR-γ. Compound 1G was synthesized by a quick and easy Knoevenagel condensation. Acute oral toxicity was found at a dose greater than 2000 mg/Kg. Compound 1G apparently produces therapeutic effects similar to those of pioglitazone, decreasing glycaemia and triglyceride levels in diabetic animals, without liver damage. Moreover, it did not cause a significant weight gain and tended to reduce polydipsia and polyphagia, while diminishing systemic inflammation related to TNF-α and IL-6. It lowered the level of endogenous antioxidant molecules such as reduced glutathione and glutathione reductase. In conclusion, 1G may be a candidate for further testing as an euglycemic agent capable of preventing the complications of diabetes.

Characterization of Phase I Hepatic Metabolites of Anti-Premature Ejaculation Drug Dapoxetine by UHPLC-ESI-Q-TOF

Determination of the metabolism pathway of xenobiotics undergoing the hepatic pass is a crucial aspect in drug development since the presence of toxic biotransformation products may result in significant side effects during the therapy. In this study, the complete hepatic metabolism pathway of dapoxetine established according to the human liver microsome assay with the use of a high-resolution LC-MS system was described. Eleven biotransformation products of dapoxetine, including eight metabolites not reported in the literature so far, were detected and identified. N-dealkylation, hydroxylation, N-oxidation and dearylation were found to be the main metabolic reactions for the investigated xenobiotic. In silico analysis of toxicity revealed that the reaction of didesmethylation may contribute to the increased carcinogenic potential of dapoxetine metabolites. On the other hand, N-oxidation and aromatic hydroxylation biotransformation reactions possibly lead to the formation of mutagenic compounds.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset

Regulatory agencies world-wide face the challenge of performing risk-based prioritization of thousands of substances in commerce. In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk ^+/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The 'best' consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity.

Decomposition Profile Data Analysis for Deep Understanding of Multiple Effects of Natural Products

It is difficult to understand the entire effect of a natural product because such products generally have multiple effects. We propose a strategy to understand these effects effectively by decomposing them with a profile data analysis method we developed. A transcriptome profile data set was obtained from a public database and analyzed. Considering their high similarity in structure and transcriptome profile, we focused on rescinnamine and syrosingopine. Decomposed effects predicted clear differences between the compounds. Two of the decomposed effects, SREBF1 activation and HDAC inhibition, were investigated experimentally because the relationship between these effects and the compounds had not yet been reported. Analyses in vitro validated these effects, and their strength was consistent with predicted scores. Moreover, the number of outliers in decomposed effects per compound was higher in natural products than in drugs in the data set, which is consistent with the nature of the effects of natural products.

Comparative study on photocatalytic degradation of the antidepressant trazodone using (Co, Fe and Ru) doped titanate nanowires: Kinetics, transformation products and in silico toxicity assessment

Titanate nanomaterials have been outstanding in the removal of emerging contaminants by the photocatalysis process. These photocatalysts, when modified through techniques such as doping with metals, they have advantages over TiO₂, especially in the region of visible light. In this work, the photocatalytic performance of four recent reported catalysts, pristine titanate nanowires, cobalt-doped titanate nanowires, iron-doped titanate nanowires and ruthenium-doped titanate nanowires, for the removal of the antidepressant trazodone under visible light radiation was compared. The iron-doped titanate nanowires presented the best catalytic activity by the catalyst surface area. Additionally, thirteen transformation products (TPs) were identified by high-resolution mass spectrometry and, to the best of our knowledge, nine of them have never been described in the literature. It was shown that for each catalyst different TPs were formed with distinct time profiles. Finally, toxicity assessment by computational methods showed that TPs were not readily biodegradable and they presented toxicity to aquatic organisms with mutagenic potential. These findings reinforce the importance of taking into consideration the TPs formed during the removal of pollutants since many of them may be toxic and can be produced during photocatalysis.

A comparison of the persistence, toxicity, and exposure to high-volume natural plant-derived and synthetic pesticides

The immobility of plants exerted evolutionary selection pressures resulting in the production of thousands of chemical substances thought to function as pesticides against predation by insects and animals. More than 10,000 plant-derived compounds have been isolated with the existence of about 100,000 such compounds postulated. In 1990, Ames et al. reported that 99.99% by weight of the pesticides ingested in a normal human diet are derived from natural plant-based sources. This surprising result raised the question as to whether these natural plant pesticides were toxic to humans. These authors examined a relatively small subset of natural pesticides and determined that their tumorigenicity in rodent cancer bioassays was similar to synthetic pesticides. In this analysis, we used standard United States Environmental Protection Agency programs to estimate the toxicity (T.E.S.T. 4.2) and persistence (EPI Suite 4.1) of a series of high-volume synthetic and natural pesticides. On average, synthetic pesticides were more persistent in the environment than were natural pesticides. This result is consistent with cost, time, and logistical constraints under which farmers apply a limited number of applications of pesticides during a crop cycle. Synthetic and natural pesticides are predicted to possess toxicities including mutagenicity and developmental toxicity. Synthetic pesticides are less often mutagenic.

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project

To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including literature survey, application of QSARs and development of Read Across methodologies. This paper summarizes the main results. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test. The reliability of the models for other assays/endpoints appears to be still far from optimality. Two new Read Across approaches were evaluated: Read Across was largely successful for predicting the Ames test results, but less for in vitro Chromosomal Aberrations. The worse results for non-Ames endpoints may be attributable to the several revisions of experimental protocols and evaluation criteria of results, that have made the databases qualitatively non-homogeneous and poorly suitable for modeling. Last, Parent/Metabolite structural differences (besides known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. The findings from this work are suitable for being integrated into Weight-of-Evidence and Tiered evaluation schemes. Areas needing further developments are pointed out.

Quantitative weight of evidence method for combining predictions of quantitative structure-activity relationship models

A method for combining statistical-based QSAR predictions of two or more binary classification models is presented. It was assumed that all models were independent. This facilitated the combination of positive and negative predictions using a quantitative weight of evidence (qWoE) procedure based on Bayesian statistics and the additivity of the logarithms of the likelihood ratios. Previous studies combined more than one prediction but used arbitrary strengths for positive and negative predictions. In our approach, the combined models were validated by determining the sensitivity and specificity values, which are performance metrics that are a point of departure for obtaining values that measure the weight of evidence of positive and negative predictions. The developed method was experimentally applied in the prediction of Ames mutagenicity. The method achieved a similar accuracy to that of the experimental Ames test for this endpoint when the overall prediction was determined using a combination of the individual predictions of more than one model. Calculating the qWoE value would reduce the requirement for expert knowledge and decrease the subjectivity of the prediction. This method could be applied to other endpoints such as developmental toxicity and skin sensitisation with binary classification models.

New in silico models to predict in vitro micronucleus induction as marker of genotoxicity

The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.

Photocatalytic degradation of cyclophosphamide and ifosfamide: Effects of wastewater matrix, transformation products and in silico toxicity prediction

Antineoplastic drugs have been identified in surface water and effluents from wastewater treatment and, once in the environment, may be harmful to aquatic organisms, as these compounds are possibly mutagenic, genotoxic, cytotoxic, carcinogenic and teratogenic. This work investigated the photodegradation of cyclophosphamide (CP) and ifosfamide (IF) using ruthenium doped titanate nanowires (Ru-TNW) in distilled water (DW) and in wastewater (WW) from secondary wastewater treatment, under UV-Vis radiation. The results indicated that Ru-TNW showed photocatalytic activity for the two cytotoxic drugs with the half-life (t_1/2) of 15.1 min for CP and 12.9 min for IF in WW. Four CP transformation products (TPs) and six IF TPs from the photodegradation process are here reported. These TPs were elucidated by high-resolution mass spectrometry. For both pollutants, the results showed different time profiles for the TPs when WW and DW were used as matrix. Overall, in the WW there was a higher production of TPs and two of them were detected only in this matrix. In other words, environmental matrices may produce different TPs. Degradation pathways were proposed and both drugs bear similarities. Additionally, in silico toxicity were performed by quantitative structure-activity relationship models. The predictions indicated that the TPs, with the exception of one IF TP, presented high mutagenic potential.

In Silico Approaches in Predictive Genetic Toxicology

Genetic toxicology testing is a weight-of-evidence approach to identify and characterize chemical substances that can cause genetic modifications in somatic and/or germ cells. Prediction of genetic toxicology using computational tools is gaining more attention and preferred by regulatory authorities as an alternate safety assessment for in vivo or in vitro approaches. Due to the cost and time associated with experimental genetic toxicity tests, it is essential to develop more robust in silico methods to predict chemical genetic toxicity. A number of in silico genotoxicity predictive tools/models are developed based on the experimental data gathered over the years. These in silico tools are divided into statistical quantitative structure-activity relationships (QSAR)-based approaches and expert-based systems. This chapter covers the state of the art in silico toxicology approaches and standardized protocols, essential for conducting genetic toxicity predictions of chemicals. This chapter also highlights various parameters for the validation of the prediction results obtained from QSAR models.

Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data

Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

Novel tetrahydroacridine derivatives with iodobenzoic moieties induce G0/G1 cell cycle arrest and apoptosis in A549 non-small lung cancer and HT-29 colorectal cancer cells

A series of nine tetrahydroacridine derivatives with iodobenzoic moiety were synthesized and evaluated for their cytotoxic activity against cancer cell lines—A549 (human lung adenocarcinoma), HT-29 (human colorectal adenocarcinoma) and somatic cell line—EA.hy926 (human umbilical vein cell line). All compounds displayed high cytotoxicity activity against A549 (IC₅₀ 59.12–14.87 µM) and HT-29 (IC₅₀ 17.32–5.90 µM) cell lines, higher than control agents—etoposide and 5-fluorouracil. Structure–activity relationship showed that the position of iodine in the substituent in the para position and longer linker most strongly enhanced the cytotoxic effect. Among derivatives, 1i turned out to be the most cytotoxic and displayed IC₅₀ values of 14.87 µM against A549 and 5.90 µM against HT-29 cell lines. In hyaluronidase inhibition assay, all compounds presented anti-inflammatory activity, however, slightly lower than reference compound. ADMET prediction showed that almost all compounds had good pharmacokinetic profiles. 1b, 1c and 1f compounds turned out to act against chemoresistance in cisplatin-resistant 253J B-V cells. Compounds intercalated into DNA and inhibited cell cycle in G0/G1 phase—the strongest inhibition was observed for 1i in A549 and 1c in HT-29. Among compounds, the highest apoptotic effect in both cell lines was observed after treatment with 1i. Compounds caused DNA damage and H2AX phosphorylation, which was detected in A549 and HT-29 cells. All research confirmed anticancer properties of novel tetrahydroacridine derivatives and explained a few pathways of their mechanism of cytotoxic action.

Screening of terpenoids as potential therapeutics against Zaire ebolavirus infection through pharmacophore-based drug design

Backgroud: Ebola virus disease (EVD) has spread to various countries in the world and has caused many deaths. Five different virus species can cause EVD, but the most virulent is Zaire ebolavirus (EBOV). The genome of EBOV includes seven genes that encode proteins playing essential roles in the virus lifecycle. Among these proteins, VP24 plays a vital role in the inhibition of the host cells’ immune system. Therefore, VP24 is a potential target for EVD therapy. In the present study, a potential inhibitor of EBOV VP24 activity was identified through pharmacophore-based drug design. Methods: This research was a in silico study, using pharmacophore based molecular docking simulation to obtain inhibitor candidates. Result: Terpenoids were used as VP24 inhibitor candidates. In particular, 55,979 terpenoids were obtained from the PubChem database. An initial screening based on the toxicity prediction test was performed with DataWarrior software: 3,353 ligands were shown to have a favorable toxicity profile, but only 1,375 among them had suitable pharmacophore features. These ligands were used for pharmacophore-based rigid and flexible molecular docking simulations with PDB ID: 4M0Q, chosen as the crystal structure of EBOV VP24. Six ligands predicted to have strong molecular interactions with EBOV VP24 underwent pharmacological property analysis through various software packages, including DataWarrior, SwissADME, admetSAR, pkCSM, and Toxtree. Conclusions: Taxumairol V was identified as the best candidate for EVD drug therapy via EBOV VP24 inhibition based on its molecular properties, predicted molecular interactions with the target molecule, and predicted pharmacological properties.

Rapid degradation of clozapine by heterogeneous photocatalysis. Comparison with direct photolysis, kinetics, identification of transformation products and scavenger study

In this study TiO₂-mediated photocatalytic degradation of the persistent drug clozapine under the simulated solar radiation was studied for the first time. The experiments were conducted both in the ultrapure and river water, which enabled the assessment of the organic matrix impact. The direct and indirect photolysis experiments were conducted for a comparison. Influence of the catalyst loading on the efficiency of the process was also assessed, and the highest catalyst loading (300 mg L^-1) was found to be the most effective. The TiO₂ photocatalysis was extremely effective for clozapine degradation - the decomposition was almost 300 times faster in comparison to the direct photolysis (t_1/2 = 1.7 min, neither clozapine, nor the intermediates were detected after 20 min of irradiation), and presence of the organic matrix did not negatively affect the process. Nevertheless the photocatalytic process turned out to be highly sensitive to act of the ROS scavengers. Thirteen transformation products (TPs) were found and their structures were elucidated by the means of high resolution mass spectrometry. Properties - toxicity, biodegradability, BCF and BAF - of TPs and the parent molecule were estimated with the use of computational methods. Identified TPs were found as generally less toxic and more biodegradable than clozapine.

In silico prediction of chromosome damage: comparison of three (Q)SAR models

Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure-activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction.

In silico model for mutagenicity (Ames test), taking into account metabolism

Bacterial reverse mutation test is one of the most common methods used to address genotoxicity. The experimental test is designed to include a step to simulate mammalian metabolism. The most common metabolic activation system is the incubation with S9 fraction prepared from the livers of rodents. Usually, in silico models addressing this endpoint are developed on the basis of an overall call disregarding the fact that the toxic effect was observed before or after metabolic activation. Here, we present a new in silico model to predict mutagenicity as measured by activity in the bacterial reverse mutation test, bearing in mind the role of S9 activation to stimulate metabolism. We applied the software SARpy, which identifies structural alerts associated with the effect. Different rules codified by these structural alerts were found in case of positive or negative mutagenicity, observed in the presence or absence of the S9 fraction. These rules can be used to understand the role of metabolism in mutagenicity better. We also identified a possible association of the results from these models with carcinogenicity.

Photolytic and photocatalytic transformation of an antipsychotic drug asenapine: Comparison of kinetics, identification of transformation products, and in silico estimation of their properties

The photolytic and photocatalytic transformation of an antipsychotic drug asenapine with the use of H₂O₂ and TiO₂ was studied. A method employing irradiation with a simulated full solar spectrum in the photostability chamber was applied, then the reverse-phase ultra high performance liquid chromatography with diode array detector, coupled with electrospray quadrupole time-of-flight mass spectrometer (RP-UHPLC-DAD - ESI-Q-TOF) was used for the quantitative and qualitative analysis of the processes. The developed quantitative method was fully validated, according to the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidelines, and the kinetic parameters of asenapine photodecomposition were compared. Nineteen phototransformation products were detected, and their probable structures - mainly hydroxylated and oxidized asenapine derivatives - were suggested. On the basis of the elucidated structures the computational prediction of their toxicity at the various endpoints, as well as bioconcentration factors and biodegradability was performed. The obtained results were then subjected to the principal component analysis (PCA). This chemometric technique facilitated comparison of the applied models, calculated properties of the TPs, and enabled visualization of relationships between them.

Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties

The genetic toxicology of nanomaterials is a crucial toxicology issue and one of the least investigated topics. Substantially, the genotoxicity of metal oxide nanomaterials' data is resulting from in vitro comet assay. Current contributions to the genotoxicity data assessed by the comet assay provide a case-by-case evaluation of different types of metal oxides. The existing inconsistency in the literature regarding the genotoxicity testing data requires intelligent assessment strategies, such as weight of evidence evaluation. Two main tasks were performed in the present study. First, the genotoxicity data from comet assay for 16 noncoated metal oxide nanomaterials with different core composition were collected. An evaluation criterion was applied to establish which of these individual lines of evidence were of sufficient quality and what weight could have been given to them in inferring genotoxic results. The collected data were surveyed on (1) minimum necessary characterization points for nanomaterials and (2) principals of correct comet assay testing for nanomaterials. Second, in this study the genotoxicity effect of metal oxide nanomaterials was investigated by quantitative nanostructure-activity relationship approach. A set of quantum-chemical descriptors was developed for all investigated metal oxide nanomaterials. A classification model based on decision tree was developed for the investigated dataset. Thus, three descriptors were identified as the most responsible factors for genotoxicity effect: heat of formation, molecular weight, and surface area of the oxide cluster based on the conductor-like screening model. Conclusively, the proposed genotoxicity assessment strategy is useful to prioritize the study of the nanomaterials for further risk assessment evaluations.

Predicting mammalian metabolism and toxicity of pesticides in silico

Pesticides must be effective to be commercially viable but they must also be reasonably safe for those who manufacture them, apply them, or consume the food they are used to produce. Animal testing is key to ensuring safety, but it comes late in the agrochemical development process, is expensive, and requires relatively large amounts of material. Surrogate assays used as in vitro models require less material and shift identification of potential mammalian toxicity back to earlier stages in development. Modern in silico methods are cost-effective complements to such in vitro models that make it possible to predict mammalian metabolism, toxicity and exposure for a pesticide, crop residue or other metabolite before it has been synthesized. Their broader use could substantially reduce the amount of time and effort wasted in pesticide development. This contribution reviews the kind of in silico models that are currently available for vetting ideas about what to synthesize and how to focus development efforts; the limitations of those models; and the practical considerations that have slowed development in the area. Detailed discussions are provided of how bacterial mutagenicity, human cytochrome P450 (CYP) metabolism, and bioavailability in humans and rats can be predicted. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems

Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.

Photolytic and photocatalytic degradation of tandospirone: Determination of kinetics, identification of transformation products and in silico estimation of toxicity

The photolytic and photocatalytic transformation of tandospirone with the use of TiO₂ and H₂O₂ was investigated. A micro-scale method for simultaneous irradiation with simulated full solar spectrum of multiple samples in photostability chamber was proposed. RP-UHPLC-DAD coupled with ESI-Q-TOF mass spectrometer was used for the quantitative and qualitative analysis of the processes. The developed method was fully validated and the kinetic parameters of tandospirone photodegradation were compared. The structures of eighteen photoproducts as well as phototransformation pathways were proposed. Based on the elucidated structures, computational toxicity assessment with the use of various software was performed and most of the photoproducts were found as less or similarly toxic to the parent compound. Nevertheless, several products, including one of the drug main metabolites, were significantly more toxic than the parent drug. The multivariate chemometric method (principal component analysis) was used to compare the toxicity of phototransformation products as well as the toxicity of the assessment methods.

Genotoxicity assessment of piperitenone oxide: An in vitro and in silico evaluation

Piperitenone oxide, a natural flavouring agent also known as rotundifolone, has been studied for the genotoxicity assessment by an integrated in vitro and in silico experimental approach, including the bacterial reverse mutation assay, the micronucleus test, the comet assay and the computational prediction by Toxtree and VEGA tools. Under our experimental conditions, the monoterpene showed to induce both point mutations (i.e. frameshift, base-substitution and/or oxidative damage) and DNA damage (i.e. clastogenic or aneuploidic damage, or single-strand breaks). Computational prediction for piperitenone oxide agreed with the toxicological data, and highlighted the presence of the epoxide function and the α,β-unsaturated carbonyl as possible structural alerts for DNA damage. However, improving the toxicological libraries for natural occurring compounds is required in order to favour the applicability of in silico models to the toxicological predictions. Further in vivo evaluations are strictly needed in order to evaluate the role of the bioavailability of the substance and the metabolic fate on its genotoxicity profile. To the best of our knowledge, these data represent the first evaluation of the genotoxicity for this flavour compound and suggest the need of further studies to assess the safety of piperitenone oxide as a flavouring agent.

Design, Synthesis and Biological Evaluation of 2-(2-Amino-5(6)-nitro-1H-benzimidazol-1-yl)-N-arylacetamides as Antiprotozoal Agents

Parasitic diseases are a public health problem affecting millions of people worldwide. One of the scaffolds used in several drugs for the treatment of parasitic diseases is the benzimidazole moiety, a heterocyclic aromatic compound. This compound is a crucial pharmacophore group and is considered a privileged structure in medicinal chemistry. In this study, the benzimidazole core served as a model for the synthesis of a series of 2-(2-amino-5(6)-nitro-1H-benzimidazol-1-yl)-N-arylacetamides 1–8 as benznidazole analogues. The in silico pharmacological results calculated with PASS platform exhibited chemical structures highly similar to known antiprotozoal drugs. Compounds 1–8 when evaluated in silico for acute toxicity by oral dosing, were less toxic than benznidazole. The synthesis of compounds 1–8 were carried out through reaction of 5(6)-nitro-1H-benzimidazol-2-amine (12) with 2-chlroactemides 10a–h, in the presence of K₂CO₃ and acetonitrile as solvent, showing an inseparable mixture of two regioisomers with the -NO₂ group in position 5 or 6 with chemical yields of 60 to 94%. The prediction of the NMR spectra of molecule 1 coincided with the experimental chemical displacements of the regioisomers. Comparisons between the NMR prediction and the experimental data revealed that the regioisomer endo-1,6-NO₂ predominated in the reaction. The in vitro antiparasitic activity of these compounds on intestinal unicellular parasites (Giardia intestinalis and Entamoeba histolytica) and a urogenital tract parasite (Trichomonas vaginalis) were tested. Compound 7 showed an IC₅₀ of 3.95 μM and was 7 time more active against G. intestinalis than benznidazole. Compounds 7 and 8 showed 4 times more activity against T. vaginalis compared with benznidazole.