Emerging approaches in predictive toxicology

ProTox 3.0: a webserver for the prediction of toxicity of chemicals

Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration, such interactions can also result in adverse effects. With the increase in computational methods, the in silico methods can offer significant benefits to both regulatory needs and requirements for risk assessments and the pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox 3.0, which incorporates molecular similarity and machine-learning models for the prediction of 61 toxicity endpoints such as acute toxicity, organ toxicity, clinical toxicity, molecular-initiating events (MOE), adverse outcomes (Tox21) pathways, several other toxicological endpoints and toxicity off-targets. All the ProTox 3.0 models are validated on independent external sets and have shown strong performance. ProTox envisages itself as a complete, freely available computational platform for in silico toxicity prediction for toxicologists, regulatory agencies, computational chemists, and medicinal chemists. The ProTox 3.0 webserver is free and open to all users, and there is no login requirement and can be accessed via https://tox.charite.de. The web server takes a 2D chemical structure as input and reports the toxicological profile of the compound for each endpoint with a confidence score and overall toxicity radar plot and network plot.

In-silico green toxicology approach toward discovering safer ligands for development of safe-by-design metal-organic frameworks

A vast variety of chemical compounds have been fabricated and commercialized, they not only result in industrial exposure during manufacturing and usage, but also have environmental impacts throughout their whole life cycle. Consequently, attempts to assess the risk of chemicals in terms of toxicology have never ceased. In-silico toxicology, also known as predictive toxicology, has advanced significantly over the last decade as a result of the drawbacks of experimental investigations. In this study, ProTox-III was applied to predict the toxicity of the ligands used for metal-organic framework (MOF) design and synthesis. Initially, 35 ligands, that have been frequently utilized for MOF synthesis and fabrication, were selected. Subsequently, canonical simplified molecular-input line-entry system (SMILES) of ligands were extracted from the PUBCHEM database and inserted into the ProTox-III online server. Ultimately, webserver outputs including LD50 and the probability of toxicological endpoints (cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, and ecotoxicity) were obtained and organized. According to retrieved LD50 data, the safest ligand was 5-hydroxyisophthalic. In contrast, the most hazardous ligand was 5-chlorobenzimidazole, with an LD50 of 8 mg/kg. Among evaluated endpoints, ecotoxicity was the most active and was detected in several imidazolate ligands. This data can open new horizons in design and development of green MOFs.

In silico toxicity and immunological interactions of components of calcium silicate-based and epoxy resin-based endodontic sealers

OBJECTIVES

The present study aimed to determine in silico toxicity predictions of test compounds from hydraulic calcium silicate-based sealers (HCSBS) and AH Plus and computationally simulate the interaction between these substances and mediators of periapical inflammation via molecular docking.

MATERIALS AND METHODS

All chemical information of the test compounds was obtained from the PubChem site. Predictions for bioavailability and toxicity analyses were determined by the Molinspiration Cheminformatics, pkCSM, ProTox-II and OSIRIS Property Explorer platforms. Molecular docking was performed using the Autodock4 AMDock v.1.5.2 program to analyse interactions between proteins (IL-1β, IL-6, IL-8, IL-10 and TNF-α) and ligands (calcium silicate hydrate, zirconium oxide, bisphenol-A epoxy resin, dibenzylamine, iron oxide and calcium tungstate) to establish the affinity and bonding mode between systems.

RESULTS

Bisphenol-A epoxy resin had the lowest maximum dose tolerated in humans and was the test compound with the largest number of toxicological properties (hepatotoxicity, carcinogenicity and irritant). All systems had favourable molecular docking. However, the ligands bisphenol-A epoxy resin and dibenzylamine had the greatest affinity with the cytokines tested.

CONCLUSION

In silico predictions and molecular docking pointed the higher toxicity and greater interaction with mediators of periapical inflammation of the main test compounds from AH Plus compared to those from HCSBS.

CLINICAL RELEVANCE

This is the first in silico study involving endodontic materials and may serve as the basis for further research that can generate more data, producing knowledge on the interference of each chemical compound in the composition of different root canal sealers.

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

Evaluation of potential drugs against leishmaniasis targeting catalytic subunit of Leishmania donovani nuclear DNA primase using ligand based virtual screening, docking and molecular dynamics approaches

Leishmania donovani, causes leishmaniasis, a global health trouble with around 89 different countries and its population under its risk. Replication initiation events have been instrumental in regulating the DNA duplication and as the small subunit of L. donovani nuclear DNA primase (Ld-PriS) inherits the catalytic site, it plays a vital role in DNA replication. In this study we have aimed Ld-PriS for the first time as a prospective target for the application of drug against Leishmania parasite. 3-D structures of Ld-PriS were built and ligand-based virtual screening was performed using hybrid similarity recognition techniques. Ligands from the ZINC database were used for the screening purposes based on known DNA primase inhibitor Sphingosine as a query. Top 150 ligands were taken into consideration for molecular docking against the query protein (Ld-PriS) using PyRx and iGEMDOCK softwares. Top five compounds with the best docking score were selected for pharmacokinetic investigation and molecular dynamic simulation. These top five screened inhibitors showed very poor binding affinity toward the catalytic subunit of human primase indicating their safety toward the host normal replication mechanism. The top five compounds showed good pharmacokinetic profiles and ADMET predictions revealed good absorption, solubility, permeability, uniform distribution, proper metabolism, minimal toxicity and good bioavailability. Simulation studies upto 50 ns revealed the three leads ZINC000009219046, ZINC000025998119 and ZINC000004677901 bind with Ld-PriS throughout the simulation and there were no huge variations in their backbone suggesting that these three may play as potential lead compounds for developing new drug against leishmaniasis.Communicated by Ramaswamy H. Sarma.

Evaluation of Pb concentrations in selected vegetables and portable drinking water, and intelligent quotients of school children in Ishiagu-a Pb mining community: health risk assessment using predictive modelling

This study evaluates the concentrations of lead (Pb) in 6 selected vegetables and drinking water samples taken from an agricultural/mining town Ishiagu. This evaluation is important because these vegetables and water are major gateway of lead exposure through ingestion, especially children in the Pb mining environment. Pb at even very low concentrations has been shown to have adverse effect on developing brain and hence children's intellectual ability. The impact of lead-contaminated food/water intake on the cognitive function was focused on school children whose parents have lived in the Pb mining town for over 25 years before they were born. Non-invasive, "target risk quotient" (TRQ) methodology, based on the principle of predictive toxicology was adopted for our analysis. Samples of these vegetables harvested in July and August 2015, and water taken from homes at 4 different villages in Ishiagu town and neighbouring community Akaeze (control), were subjected to appropriate chemical treatment/digestion procedures and the concentrations of Pb determined using AA-700 Shimadzu model atomic absorption spectrophotometer. From 642 structured questionnaire administered to the teachers/children, the daily vegetable ingestion rates for each vegetable (mg/child/day) and estimated daily intakes (EDI) of lead were obtained. The results show that the concentrations of Pb in water samples and the 6 vegetables harvested from the lead mining town vary as distances increase from the mining sites while the total target hazard quotients (TTHQs) for the vegetable crops were greater than one (˃ 1). The cognitive functions of 160 school children (aged 6-8 years), sampled from 265 families based on their meeting the criteria for distances away from the mining site, were evaluated using Raven's Standard Progressive Matrices and psychometrics. The data generated were analysed using (SPSS) version 21.0 and results expressed as mean ± standard deviation of intelligent quotient (IQ). Students' t tests for independent samples were used to compare the IQ results for children in the lead mining area and non-mining area. A model based on predictive toxicology paradigm which can show a relationship between concentrations of lead in vegetables/water and cognitive function was developed. This model shows that there is a positive correlation between total lead concentrations in vegetables/water and children's cognitive function.

ProTox-II: a webserver for the prediction of toxicity of chemicals

Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology

As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.

ProTox-II: a webserver for the prediction of toxicity of chemicals

Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions

BACKGROUND

The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions.

OBJECTIVE

Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures.

METHODS

New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.

DISCUSSION

NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams.

CONCLUSIONS

While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.

In vitro genotoxicity testing-Can the performance be enhanced?

The assessment of genotoxicity represents an essential component of the safety assessment of all types of substances. Several in vitro tests are available at different stages of development and acceptance, yet they are not considered at present sufficient to fully replace animal tests needed to evaluate the safety of substances. For an overall improvement of the traditional genotoxicity testing paradigm, several recent activities have taken place. These include the improvement of existing tests, the development of novel tests, as well as, the establishment and exploration of approaches to optimise in vitro testing accuracy. Furthermore, useful tools, such as databases or reference chemical lists have been developed to support advances in this field.

Identification of Genes That Modulate Susceptibility to Formaldehyde and Imatinib by Functional Genomic Screening in Human Haploid KBM7 Cells

Though current functional genomic screening systems are useful for investigating human susceptibility to chemical toxicity, they have limitations. Well-established, high-throughput yeast mutant screens identify only evolutionarily conserved processes. RNA interference can be applied in human cells but is limited by incomplete gene knockout and off-target effects. Human haploid cell screening is advantageous as it requires knockdown of only a single copy of each gene. A human haploid cell mutant library (KBM7-Mu), derived from a chronic myeloid leukemia (CML) patient, was recently developed and has been used to identify genes that modulate sensitivity to infectious agents and pharmaceutical drugs. Here, we sought to improve the KBM7-Mu screening process to enable efficient screening of environmental chemicals. We developed a semi-solid medium based screening approach that cultures individual mutant colonies from chemically resistant cells, faster (by 2-3 weeks) and with less labor than the original liquid medium-based approach. As proof of principle, we identified genetic mutants that confer resistance to the carcinogen formaldehyde (FA, 12 genes, 18 hits) and the CML chemotherapeutic agent imatinib (6 genes, 13 hits). Validation experiments conducted on KBM7 mutants lacking each of the 18 genes confirmed resistance of 6 FA mutants (CTC1, FCRLA, GOT1, LPR5, M1AP, and MAP2K5) and 1 imatinib-resistant mutant (LYRM9). Despite the improvements to the method, it remains technically challenging to limit false positive findings. Nonetheless, our findings demonstrate the broad applicability of this optimized haploid approach to screen toxic chemicals to identify novel susceptibility genes and gain insight into potential mechanisms of toxicity.

Intersection of toxicogenomics and high throughput screening in the Tox21 program: an NIEHS perspective

Humans are exposed to thousands of chemicals with inadequate toxicological data. Advances in computational toxicology, robotic high throughput screening (HTS), and genome-wide expression have been integrated into the Tox21 program to better predict the toxicological effects of chemicals. Tox21 is a collaboration among US government agencies initiated in 2008 that aims to shift chemical hazard assessment from traditional animal toxicology to target-specific, mechanism-based, biological observations using in vitro assays and lower organism models. HTS uses biocomputational methods for probing thousands of chemicals in in vitro assays for gene-pathway response patterns predictive of adverse human health outcomes. In 1999, NIEHS began exploring the application of toxicogenomics to toxicology and recent advances in NextGen sequencing should greatly enhance the biological content obtained from HTS platforms. We foresee an intersection of new technologies in toxicogenomics and HTS as an innovative development in Tox21. Tox21 goals, priorities, progress, and challenges will be reviewed.