Genetic toxicology in silico protocol

Hasselgren, Catrin; Ahlberg, Ernst; Akahori, Yumi; Amberg, Alexander; Anger, Lennart T.; Atienzar, Franck; Auerbach, Scott S.; Beilke, Lisa; Bellion, Phillip; Benigni, Romualdo; Bercu, Joel P.; Booth, Ewan D.; Bower, David; Brigo, Alessandro; Cammerer, Zoryana; Cronin, Mark T.D.; Crooks, Ian; Cross, Kevin P.; Custer, Laura; Dobo, Krista L.; Doktorova, Tatyana Y.; Faulkner, David; Ford, Kevin A.; Fortin, Marie; Frericks, Markus; Gad-McDonald, Samantha; Gellatly, Nichola; Gerets, Helga; Gervais, Véronique; Glowienke, Susanne; Gompel, Jacky Van; Harvey, James S.; Hillegass, Jedd M.; Honma, Masamitsu; Hsieh, Jui-Hua; Hsu, Chia-Wen; Barton-Maclaren, Tara S.; Johnson, Candice; Jolly, Robert A.; Jones, David R.; Kemper, Ray; Kenyon, Michelle; Kruhlak, Naomi L.; Kulkarni, Sunil; Kümmerer, Klaus; Leavitt, Penny; Masten, Scott A.; Miller, Scott A.; Moudgal, Chandrika; Muster, Wolfgang; Paulino, Alexandre Tadeu; Piparo, Elena Lo; Powley, Mark W.; Quigley, Donald P.; Reddy, M. Vijayaray; Richarz, Andrea-Nicole; Schilter, Benoı̂t; Snyder, Ronald D.; Stavitskaya, Lidiya; Stidl, Reinhard; Szabo, David T.; Teasdale, Andrew; Tice, Raymond R.; Trejo‐Martin, Alejandra; Vuorinen, Anna; Wall, B.; Watts, Pete; White, Angela; Wichard, Joerg; Witt, Kristine L.; Woolley, Adam; Woolley, David; Zwickl, Craig; Myatt, Glenn J.

doi:10.1016/j.yrtph.2019.104403

Cited by 69 publications

(30 citation statements)

References 72 publications

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“…In this work, assessments of toxicological potential were performed using the program ToxTree, and the Nexus software package was used to identify mechanisms that might contribute to the predicted toxicity. Knowledge generated in this way can be used to guide and support targeted in vitro testing ( Myatt et al, 2018 ; Hasselgren et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Toxicological testing of syringaresinol and enterolignans

Kirsch

Bakuradze

Richling

2020

Current Research in Toxicology

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Lignans are secondary plant constituents with dibenzylbutane skeletons found in cereals, oilseeds, and nuts. Two members of this class, syringaresinol (Syr) and secoisolariciresinol (Seco), occur at relatively high levels in cereals and processed food products as well as in coniferous trees . In vitro studies have shown that Seco and its metabolites enterodiol (END) and enterolactone (ENL), which are formed by intestinal microbes, exhibit strong antioxidant activity because of their phenolic character. The biological activity and discussion of dietary supplementation with these substances led to questions about the potential adverse health effects of these compounds, which are explored here. Syr and the metabolites END and ENL were investigated by combining structural information generated in silico with practical testing in vitro . An in silico structure-activity analysis was performed using ToxTree and NexusPrediction to suggest plausible mechanisms of toxicity and estimate toxicological endpoints of these compounds. Structural alerts were generated based on the presence of phenolic units with coordinating substituents that could potentially form quinoid structures, promote reactive oxygen species (ROS) formation, bind to cellular structures, or damage chromosomes. To assess the in silico results, the cytotoxicity and genotoxic potential of the studied compounds were tested in vitro using the resazurin reduction and comet assays, respectively. Incubating HepG2 and HT29 cells for 1 h or 24 h with 0–100 μM Syr, END, or ENL induced no cytotoxic effects. Additionally, even the highest tested concentrations of END and ENL showed no modulation of background and total DNA damage. The initial in silico screen thus generated structural alerts linked to toxicological endpoints, but experimental assessments of the studied compounds revealed no detectable toxicity, demonstrating the need for individual mechanistic experimental verification of in silico predictions. This approach makes it possible to connect known biological activity, such as reported antioxidative effects, to underlying mechanisms such as proton abstraction or donation. This in turn can yield insights – for example, that a compound's tendency to act as a pro- or anti-oxidant (and hence to exert adverse or beneficial health effects) may depend on its concentration and the cellular state.

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Section: Introductionmentioning

confidence: 99%

Toxicological testing of syringaresinol and enterolignans

Kirsch

Bakuradze

Richling

2020

Current Research in Toxicology

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“…1) from the electroactive polymer‐based materials in vitro 102 . The electroactive polymers used to prepare the polymer films in this report were copolymers of oligoanilines linked to polyethyleneglycol (PEG) via ester bonds that were prepared and characterised as previously described, 101 and we extend our investigations here by reporting the application of in silico toxicity screening 103 as a potentially useful method to facilitate the development of non‐toxic degradable electroactive polymers, thereby addressing some of the commercial challenges to biomaterial development, 104 in accordance with ‘safe by design’ principles 105,106 …”

Section: Introductionmentioning

confidence: 90%

Wirelessly triggered bioactive molecule delivery from degradable electroactive polymer films

Ashton

Appen

Fırlak

et al. 2020

Polymer International

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The development of stimuli‐responsive drug delivery systems offers significant opportunities for innovations in industry. It is possible to produce polymer‐based drug delivery devices enabling spatiotemporal control of the release of the drug triggered by an electrical stimulus. Here we describe the development of a wireless controller for drug delivery from conductive/electroactive polymer‐based biomaterials and demonstrate its function in vitro. The wireless polymer conduction controller device uses very low power, operating at 2.4 GHz, and has a supply voltage controller circuit which controls electrical stimulation voltage levels. The computer graphical user interface program communicates with the controller device, and it receives device information, device status and temperature data from the controller device. The prototype of the wireless controller system can trigger the delivery of a drug, dexamethasone phosphate, from a matrix of degradable electroactive polymers. Furthermore, we introduce the application of in silico toxicity screening as a potentially useful method to facilitate the design of non‐toxic degradable electroactive polymers for a multitude of biotechnological applications, addressing one of the key commercial challenges to biomaterial development, in accordance with ‘safe by design’ principles. © 2020 The Authors. Polymer International published by John Wiley & Sons Ltd on behalf of Society of Industrial Chemistry.

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“…Details of the interaction of this molecule with FPPS were determined by molecular docking analysis. Cytotoxicity and genotoxicity were further evaluated in silico with the aim of predicting toxicological effects directly relevant to human cells, as well as to support hazard and risk assessment activities [43]. Taken together, our results strongly suggest that CHEMBL2007613 is a potential candidate for antiviral treatments.…”

Section: Introductionmentioning

confidence: 91%

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks

Puentes¹,

Valderrama²,

González³

et al. 2020

Preprint

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The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules' target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 95.8% in the Receiver Operating Characteristic curve (ROC-AUC) and 58.9% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained [3] potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.

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Genetic toxicology in silico protocol

Cited by 69 publications

References 72 publications

Toxicological testing of syringaresinol and enterolignans

Toxicological testing of syringaresinol and enterolignans

Wirelessly triggered bioactive molecule delivery from degradable electroactive polymer films

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks

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