In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

Pawar, Gopal; Madden, Judith C.; Ebbrell, David J.; Firman, James W.; Cronin, Mark T.D.

doi:10.3389/fphar.2019.00561

Cited by 61 publications

(45 citation statements)

References 198 publications

(185 reference statements)

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“…In a recent comprehensive review, over 900 databases were identified and characterised in terms of the type of information available, as well as their public or commercial accessibility, interoperability, search criteria, etc. 17 The categories for the types of database considered (with the number of associated databases given in parentheses) were: biological (268); drug discovery (157); clinical trials (116); chemistry (80); omics (60); toxicology (57); protein-protein interactions (54); alternative methods (39); ADME (38); pathways (38); environmental exposure (30); nanomaterials toxicity (22); and patents (9). Of the hundreds of databases available, some representative examples of freely accessible databases are shown in Table 2, in order to indicate the nature and scope of these resources.…”

Section: Databasesmentioning

confidence: 99%

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

et al. 2020

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Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

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Section: Databasesmentioning

confidence: 99%

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

et al. 2020

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“…Therefore, as part of an effort to expand the toxicology database, a multidimensional HTS assay was devised to examine all ToxCast phase 1 and 2 chemicals (over 1,000 unique chemicals) for developmental-and neuro-toxicity in the embryonic zebrafish [7]. While computational approaches to bridge the data gap above have been developed, with Quantitative Structure-Activity Relationship (QSAR) and Read-Across being the most commonly used methodologies [8][9][10][11][12][13]. Both methods rely on the grouping of chemicals together using fragment descriptors, e.g.…”

Section: Introductionmentioning

confidence: 99%

Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology

et al. 2021

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There are currently 85,000 chemicals registered with the Environmental Protection Agency (EPA) under the Toxic Substances Control Act, but only a small fraction have measured toxicological data. To address this gap, high-throughput screening (HTS) and computational methods are vital. As part of one such HTS effort, embryonic zebrafish were used to examine a suite of morphological and mortality endpoints at six concentrations from over 1,000 unique chemicals found in the ToxCast library (phase 1 and 2). We hypothesized that by using a conditional generative adversarial network (cGAN) or deep neural networks (DNN), and leveraging this large set of toxicity data we could efficiently predict toxic outcomes of untested chemicals. Utilizing a novel method in this space, we converted the 3D structural information into a weighted set of points while retaining all information about the structure. In vivo toxicity and chemical data were used to train two neural network generators. The first was a DNN (Go-ZT) while the second utilized cGAN architecture (GAN-ZT) to train generators to produce toxicity data. Our results showed that Go-ZT significantly outperformed the cGAN, support vector machine, random forest and multilayer perceptron models in cross-validation, and when tested against an external test dataset. By combining both Go-ZT and GAN-ZT, our consensus model improved the SE, SP, PPV, and Kappa, to 71.4%, 95.9%, 71.4% and 0.673, respectively, resulting in an area under the receiver operating characteristic (AUROC) of 0.837. Considering their potential use as prescreening tools, these models could provide in vivo toxicity predictions and insight into the hundreds of thousands of untested chemicals to prioritize compounds for HT testing.

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“…The good news is that with “big data” a manifold of data sources is available to guide read‐across. Recently, Pawar et al conducted a meta‐review identifying more than 900 databases that contain data relevant for read‐across …”

Section: Expert Methodsmentioning

confidence: 99%

“…Recently, Pawar et al conducted a meta-review identifying more than 900 databases that contain data relevant for read-across. 36 Several recent case studies demonstrated that a careful read-across can be utilized to estimate the toxicities of various compound classes. [37][38][39][40][41] However, all case-studies show that the approach is highly dependent on the available data and the definition of similarity between the parent compound and the analogs.…”

Section: Read-acrossmentioning

confidence: 99%

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Hemmerich

Ecker

2020

WIREs Comput Mol Sci

109

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In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics

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In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

Cited by 61 publications

References 198 publications

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

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