Computational toxicology: an overview of the sources of data and of modelling methods

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Biomimetic microfluidic chips for toxicity assessment of environmental pollutants

Various types of pollutants widely present in environmental media, including synthetic and natural chemicals, physical pollutants such as radioactive substances, ultraviolet rays, and noise, as well as biological organisms, pose a huge threat to public health. Therefore, it is crucial to accurately and effectively explore the human physiological responses and toxicity mechanisms of pollutants to prevent diseases caused by pollutants. The emerging toxicological testing method biomimetic microfluidic chips (BMCs) exhibit great potential in environmental pollutant toxicity assessment due to their superior biomimetic properties. The BMCs are divided into cell-on-chips and organ-on-chips based on the distinctions in bionic simulation levels. Herein, we first summarize the characteristics, emergence and development history, composition and structure, and application fields of BMCs. Then, with a focus on the toxicity mechanisms of pollutants, we review the applications and advances of the BMCs in the toxicity assessment of physical, chemical, and biological pollutants, respectively, highlighting its potential and development prospects in environmental toxicology testing. Finally, the opportunities and challenges for further use of BMCs are discussed.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.

Rubber Antioxidants and Their Transformation Products: Environmental Occurrence and Potential Impact

Antioxidants are prevalently used during rubber production to improve rubber performance, delay aging, and extend service life. However, recent studies have revealed that their transformation products (TPs) could adversely affect environmental organisms and even lead to environmental events, which led to great public concern about environmental occurrence and potential impacts of rubber antioxidants and their TPs. In this review, we first summarize the category and application of rubber antioxidants in the world, and then demonstrate the formation mechanism of their TPs in the environment, emphasizing their influence on the ozone oxidative degradation. The potential toxic effects of antioxidants and their TPs are further reviewed to improve understanding of their biological health impact and environmental risks. Finally, the environmental occurrences of antioxidants and their TPs are summarized and their environmental impacts are demonstrated based on the recent studies. Due to the currently limited understanding on the toxic and biological effects of these compounds, further studies are required in order to better assess various TPs of these antioxidants and their environmental impact. To our knowledge, this is the first review on antioxidants and their TPs in the environment, which may elevate the environmental risk awareness of rubber products and their TPs in the near future.

FORUM: Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases

Motivation

Metabolomics studies aim at reporting a metabolic signature (list of metabolites) related to a particular experimental condition. These signatures are instrumental in the identification of biomarkers or classification of individuals, however their biological and physiological interpretation remains a challenge. To support this task, we introduce FORUM: a Knowledge Graph (KG) providing a semantic representation of relations between chemicals and biomedical concepts, built from a federation of life science databases and scientific literature repositories.

Results

The use of a Semantic Web framework on biological data allows us to apply ontological-based reasoning to infer new relations between entities. We show that these new relations provide different levels of abstraction and could open the path to new hypotheses. We estimate the statistical relevance of each extracted relation, explicit or inferred, using an enrichment analysis, and instantiate them as new knowledge in the KG to support results interpretation/further inquiries.

Availability and implementation

A web interface to browse and download the extracted relations, as well as a SPARQL endpoint to directly probe the whole FORUM KG, are available at https://forum-webapp.semantic-metabolomics.fr. The code needed to reproduce the triplestore is available at https://github.com/eMetaboHUB/Forum-DiseasesChem.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bayesian Network Resource for Meta-Analysis: Cellular Toxicity of Quantum Dots

A web-based resource for meta-analysis of nanomaterials toxicity is developed whereby the utility of Bayesian networks (BNs) is illustrated for exploring the cellular toxicity of Cd-containing quantum dots (QDs). BN models are developed based on a dataset compiled from 517 publications comprising 3028 cell viability data samples and 837 IC₅₀ values. BN QD toxicity (BN-QDTox) models are developed using both continuous (i.e., numerical) and categorical attributes. Using these models, the most relevant attributes identified for correlating IC₅₀ are: QD diameter, exposure time, surface ligand, shell, assay type, surface modification, and surface charge, with the addition of QD concentration for the cell viability analysis. Data exploration via BN models further enables identification of possible association rules for QDs cellular toxicity. The BN models as web-based applications can be used for rapid intelligent query of the available body of evidence for a given nanomaterial and can be readily updated as the body of knowledge expands.

Glyphaea brevis - In vitro antioxidant and in silico biological activity of major constituents and molecular docking analyses

Previous studies have revealed that leaf extracts of Glyphaea brevis possess antioxidant activity but the bioactivity and mechanisms of action of its major constituents remain unknown. This study evaluated in vitro antioxidant and free radical scavenging activities of Glyphaea brevis twigs and leaves, and probable toxicity profile, pharmacological activities and mechanisms of action of major phytoconstituents in silico. Phytochemical screening detected saponins, tannins, steroids, anthraquinones, flavonoids, terpenoids and phenolics in the extracts. HPLC fingerprinting revealed major compounds as ferulic, catechuic and coumaric acids. Twig extract contained more flavanols compared to the leaf extract while the leaf extract had more flavonol content. Extract of the twigs demonstrated higher ORAC, TEAC and FRAP compared to the leaf extract. In silico analyses predicted low acute toxicity risk and pharmacological activities which are in agreement with traditional use of the plant in the management of diseases such as dyspepsia, ulcers, chest pains, diarrhea, dysentery and sleeping sickness. The molecular docking studies revealed that coumaric acid and ferulic acid have the best binding for all proteins tested. In summary, Glyphaea brevis twigs possess higher antioxidant activity than the leaves and major constituents showed low toxicological potential and promising biological activities which support its ethnomedical use.

Computational approaches to chemical hazard assessment

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

Machine Learning Methods in Computational Toxicology

Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of their use in computational toxicology. The use of multiple linear regression, variants of naïve Bayes classifier, k-nearest neighbors, support vector machine, decision trees, ensemble learning, random forest, several types of neural networks, and deep learning is the focus of attention of this review. The role of fragment descriptors, graph mining, and graph kernels is highlighted. The application of unsupervised methods, such as Kohonen's self-organizing maps and related approaches, which allow for combining predictions with data analysis and visualization, is also considered. The necessity of applying a wide range of machine learning methods in computational toxicology is underlined.

Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles

Motivated by the recent development of quantitative structure-activity relationship (QSAR) methods in the area of nanotoxicology, we proposed an approach to develop additional descriptors based on results of first-principles calculations. For the evaluation of the biochemical activity of metallic nanoparticles, we consider two processes: ion extraction from the surface of a specimen to aqueous media and water dissociation on the surface. We performed calculations for a set of metals (Al, Fe, Cu, Ag, Au, and Pt). Taking into account the diversity of atomic structures of real metallic nanoparticles, we performed calculations for different models such as (001) and (111) surfaces, nanorods, and two different cubic nanoparticles of 0.6 and 0.3 nm size. Significant energy dependence of the processes from the selected model of nanoparticle suggests that for the correct description we should combine the calculations for several representative models. In addition to the descriptors of chemical activity of the metallic nanoparticles for the two studied processes, we propose descriptors for taking into account the dependence of chemical activity from the size and shape of nanoparticles. Routes to minimization of computational costs for these calculations are also discussed.

Alternatives to In Vivo Draize Rabbit Eye and Skin Irritation Tests with a Focus on 3D Reconstructed Human Cornea-Like Epithelium and Epidermis Models

Human eyes and skin are frequently exposed to chemicals accidentally or on purpose due to their external location. Therefore, chemicals are required to undergo the evaluation of the ocular and dermal irritancy for their safe handling and use before release into the market. Draize rabbit eye and skin irritation test developed in 1944, has been a gold standard test which was enlisted as OECD TG 404 and OECD TG 405 but it has been criticized with respect to animal welfare due to invasive and cruel procedure. To replace it, diverse alternatives have been developed: (i) For Draize eye irritation test, organotypic assay, in vitro cytotoxicity-based method, in chemico tests, in silico prediction model, and 3D reconstructed human cornea-like epithelium (RhCE); (ii) For Draize skin irritation test, in vitro cytotoxicity-based cell model, and 3D reconstructed human epidermis models (RhE). Of these, RhCE and RhE models are getting spotlight as a promising alternative with a wide applicability domain covering cosmetics and personal care products. In this review, we overviewed the current alternatives to Draize test with a focus on 3D human epithelium models to provide an insight into advancing and widening their utility.

Multiscale Modelling of Bionano Interface

We present a framework for coarse-grained modelling of the interface between foreign nanoparticles (NP) and biological fluids and membranes. Our model includes united-atom presentations of membrane lipids and globular proteins in implicit solvent, which are based on all-atom structures of the corresponding molecules and parameterised using experimental data or atomistic simulation results. The NPs are modelled by homogeneous spheres that interact with the beads of biomolecules via a central force that depends on the NP size. The proposed methodology is used to predict the adsorption energies for human blood plasma proteins on NPs of different sizes as well as the preferred orientation of the molecules upon adsorption. Our approach allows one to rank the proteins by their binding affinity to the NP, which can be used for predicting the composition of the NP-protein corona for the corresponding material. We also show how the model can be used for studying NP interaction with a lipid bilayer membrane and thus can provide a mechanistic insight for modelling NP toxicity.

Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives

The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, as well as aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects. The simulations are conducted at the atomic level and explicitly allow for a mechanistic interpretation of the results (in real-time 3D/4D), thereby complying with the Setubal principles put forward in 2002 for computational approaches to toxicology. Moreover, the underlying "ab-initio" protocol is independent from any training data and makes the approach universal with respect to the applicability domain. The VirtualToxLab runs in client-server mode and is freely available to academic and non-profit organizations. As the underlying technology yields a thermodynamic estimate of the binding affinity, the associated ligand-protein complexes have been challenged by molecular-dynamics simulations to probe their kinetic stability. Human African trypanosomiasis is a neglected tropical disease caused by two subspecies of Trypanosoma brucei. The control of this parasitic infection relies on a few chemotherapeutic agents, most of which were discovered decades ago and pose many challenges including adverse side effects, poor efficacy, and the occurrence of drug resistances. Natural products, on the other hand, offer a high potential for the discovery of new drug leads due to their chemical diversity. In this in silico study, we analyze a series of 89 natural products and derivatives displaying anti-trypanosomal activity for their potential to trigger adverse effects. Our results indicate a moderate potential for a number of those compounds to bind to nuclear receptors and thereby ease the development of endocrine disregulation. A few others would seem to inhibit enzymes of the cytochrome P450 family and, hence, sustain drug-drug interactions.

Mind the Gap! A Journey towards Computational Toxicology

Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

3D QSAR studies of hydroxylated polychlorinated biphenyls as potential xenoestrogens

Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are found in human biological samples and lack of data on their potential estrogenic activity has been a source of concern. We have extended our previous in silico 2D QSAR study through the application of advance techniques such as docking and 3D QSAR to gain insights into their estrogen receptor (ERα) binding. The results support our earlier findings that the hydroxyl group is the most important feature on the compounds; its position, orientation and surroundings in the structure are influential for the binding of OH-PCBs to ERα. This study has also revealed the following additional interactions that influence estrogenicity of these chemicals (a) the aromatic interactions of the biphenyl moieties with the receptor, (b) hydrogen bonding interactions of the p-hydroxyl group with key amino acids ARG394 and GLU353, (c) low or no electronegative substitution at para-positions of the p-hydroxyl group, (d) enhanced electrostatic interactions at the meta position on the B ring, and (e) co-planarity of the hydroxyl group on the A ring. In combination the 2D and 3D QSAR approaches have led us to the support conclusion that the hydroxyl group is the most important feature on the OH-PCB influencing the binding to estrogen receptors, and have enhanced our understanding of the mechanistic details of estrogenicity of this class of chemicals. Such in silico computational methods could serve as useful tools in risk assessment of chemicals.

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

Systems Biology and Synthetic Biology: A New Epoch for Toxicology Research

Systems biology and synthetic biology are emerging disciplines which are becoming increasingly utilised in several areas of bioscience. Toxicology is beginning to benefit from systems biology and we suggest in the future that is will also benefit from synthetic biology. Thus, a new era is on the horizon. This review illustrates how a suite of innovative techniques and tools can be applied to understanding complex health and toxicology issues. We review limitations confronted by the traditional computational approaches to toxicology and epidemiology research, using polycyclic aromatic hydrocarbons (PAHs) and their effects on adverse birth outcomes as an illustrative example. We introduce how systems toxicology (and their subdisciplines, genomic, proteomic, and metabolomic toxicology) will help to overcome such limitations. In particular, we discuss the advantages and disadvantages of mathematical frameworks that computationally represent biological systems. Finally, we discuss the nascent discipline of synthetic biology and highlight relevant toxicological centred applications of this technique, including improvements in personalised medicine. We conclude this review by presenting a number of opportunities and challenges that could shape the future of these rapidly evolving disciplines.

OpenVirtualToxLab--a platform for generating and exchanging in silico toxicity data

The VirtualToxLab is an in silico technology for estimating the toxic potential--endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity--of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound--its ability to trigger adverse effects--is derived from its computed binding affinities toward these very proteins: the computationally demanding simulations are executed in client-server model on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Applicability of computational systems biology in toxicology

Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research.

Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli

Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation).

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗

Alternative methods, including quantitative structure-activity relationships (QSAR), are being used increasingly when appropriate data for toxicity evaluation of chemicals are not available. Approximately 40 mono-hydroxylated polychlorinated biphenyls (OH-PCBs) have been identified in humans. They represent a health and environmental concern because some of them have been shown to have agonist or antagonist interactions with human hormone receptors. This could lead to modulation of steroid hormone receptor pathways and endocrine system disruption. We performed QSAR analyses using available estrogenic activity (human estrogen receptor ER alpha) data for 71 OH-PCBs. The modelling was performed using multiple molecular descriptors including electronic, molecular, constitutional, topological, and geometrical endpoints. Multiple linear regressions and recursive partitioning were used to best fit descriptors. The results show that the position of the hydroxyl substitution, polarizability, and meta adjacent un-substituted carbon pairs at the phenolic ring contribute towards greater estrogenic activity for these chemicals. These comparative QSAR models may be used for predictive toxicity, and identification of health consequences of PCB metabolites that lack empirical data. Such information will help prioritize such molecules for additional testing, guide future basic laboratory research studies, and help the health/risk assessment community understand the complex nature of chemical mixtures.

iFad: an integrative factor analysis model for drug-pathway association inference

MOTIVATION

Pathway-based drug discovery considers the therapeutic effects of compounds in the global physiological environment. This approach has been gaining popularity in recent years because the target pathways and mechanism of action for many compounds are still unknown, and there are also some unexpected off-target effects. Therefore, the inference of drug-pathway associations is a crucial step to fully realize the potential of system-based pharmacological research. Transcriptome data offer valuable information on drug-pathway targets because the pathway activities may be reflected through gene expression levels. Hence, it is of great interest to jointly analyze the drug sensitivity and gene expression data from the same set of samples to investigate the gene-pathway-drug-pathway associations.

RESULTS

We have developed iFad, a Bayesian sparse factor analysis model to jointly analyze the paired gene expression and drug sensitivity datasets measured across the same panel of samples. The model enables direct incorporation of prior knowledge regarding gene-pathway and/or drug-pathway associations to aid the discovery of new association relationships. We use a collapsed Gibbs sampling algorithm for inference. Satisfactory performance of the proposed model was found for both simulated datasets and real data collected on the NCI-60 cell lines. Our results suggest that iFad is a promising approach for the identification of drug targets. This model also provides a general statistical framework for pathway-based integrative analysis of other types of -omics data.

AVAILABILITY

The R package 'iFad' and real NCI-60 dataset used are available at http://bioinformatics.med.yale.edu/group.

Paradigm shift in toxicity testing and modeling

The limitations of traditional toxicity testing characterized by high-cost animal models with low-throughput readouts, inconsistent responses, ethical issues, and extrapolability to humans call for alternative strategies for chemical risk assessment. A new strategy using in vitro human cell-based assays has been designed to identify key toxicity pathways and molecular mechanisms leading to the prediction of an in vivo response. The emergence of quantitative high-throughput screening (qHTS) technology has proved to be an efficient way to decompose complex toxicological end points to specific pathways of targeted organs. In addition, qHTS has made a significant impact on computational toxicology in two aspects. First, the ease of mechanism of action identification brought about by in vitro assays has enhanced the simplicity and effectiveness of machine learning, and second, the high-throughput nature and high reproducibility of qHTS have greatly improved the data quality and increased the quantity of training datasets available for predictive model construction. In this review, the benefits of qHTS routinely used in the US Tox21 program will be highlighted. Quantitative structure-activity relationships models built on traditional in vivo data and new qHTS data will be compared and analyzed. In conjunction with the transition from the pilot phase to the production phase of the Tox21 program, more qHTS data will be made available that will enrich the data pool for predictive toxicology. It is perceivable that new in silico toxicity models based on high-quality qHTS data will achieve unprecedented reliability and robustness, thus becoming a valuable tool for risk assessment and drug discovery.

VirtualToxLab - a platform for estimating the toxic potential of drugs, chemicals and natural products

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on http://www.virtualtoxlab.org. The free platform - the OpenVirtualToxLab - is accessible (in client-server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.

Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data

Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR-like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage.

A review of toxicogenomic approaches in developmental toxicology

Over the past decade, the use of gene expression profiling (i.e., toxicogenomics or transcriptomics) has been established as the vanguard "omics" technology to investigate exposure-induced molecular changes that underlie the development of disease. As this technology quickly advances, researchers are striving to keep pace in grasping the complexity of toxicogenomic response while at the same time determine its applicability for the field of developmental toxicology. Initial studies suggest toxicogenomics to be a promising tool for multiple types of study designs, including exposure-response investigations (dose and duration), chemical classification, and model comparisons. In this review, we examine the use of toxicogenomics in developmental toxicology, discussing biological and technical factors that influence response and interpretation. Additionally, we provide a framework to guide toxicogenomic investigations in the field of developmental toxicology.

Challenges for computational structure-activity modelling for predicting chemical toxicity: future improvements?

INTRODUCTION

Structure-activity modelling for predicting toxicology as a discipline is now 50 years old, and great strides have been taken in developing methods for the physicochemical analysis of molecules and their toxicity evaluation, both essential stages in modelling. Computational toxicology also has huge potential for speeding up the screening and prioritisation of chemicals for further testing and for reducing the numbers of expensive and time-consuming conventional tests. Yet, the realisation of this potential has been largely stifled by many problems inherent in developing and validating new structure-activity models of toxicity.

AREAS COVERED

Problems of computational toxicology discussed include i) the use of inappropriate molecular descriptors and tools that are not transparent; ii) the undetected existence of chemicals that cause large changes in toxicity with only small differences in molecular structure (causing 'activity cliffs' in the structure-activity landscape); iii) spurious correlations between structure and activity; iv) lack of quality control of toxicity data; v) difficulties in determining predictivity for novel chemicals; and vi) an over-reliance on complex mathematics and statistics.

EXPERT OPINION

Greater emphasis needs to be placed on i) the selection of training and test sets of chemicals to enable both internal and external validation of models to be undertaken for more accurate assessment of model predictivity and ii) the use of recently developed techniques for characterising structure-activity landscapes.

In silico toxicology - non-testing methods

In silico toxicology in its broadest sense means “anything that we can do with a computer in toxicology.” Many different types of in silico methods have been developed to characterize and predict toxic outcomes in humans and environment. The term non-testing methods denote grouping approaches, structure–activity relationship, and expert systems. These methods are already used for regulatory purposes and it is anticipated that their role will be much more prominent in the near future. This Perspective will delineate the basic principles of non-testing methods and evaluate their role in current and future risk assessment of chemical compounds.

Informatics, machine learning and computational medicinal chemistry

This article reviews the use of informatics and computational chemistry methods in medicinal chemistry, with special consideration of how computational techniques can be adapted and extended to obtain more and higher-quality information. Special consideration is given to the computation of protein–ligand binding affinities, to the prediction of off-target bioactivities, bioactivity spectra and computational toxicology, and also to calculating absorption-, distribution-, metabolism- and excretion-relevant properties, such as solubility.

Computational toxicology: realizing the promise of the toxicity testing in the 21st century

BACKGROUND

The National Academies' Standing Committee on Use of Emerging Science for Environmental Health Decisions held a meeting (21-22 September 2009 in Washington, DC) titled "Computational Toxicology: From Data to Analyses to Applications." This commentary reflects on the presentations and roundtable discussions from the meeting that were designed to review the state of the art in the field and the practical applications of the new science and to provide focus to the field.

OBJECTIVES

The meeting considered two topics: the emerging data streams amenable to computational modeling and data mining, and the emerging data analysis and modeling tools.

DISCUSSION

Computational toxicology is a subdiscipline of toxicology that aims to use the mathematical, statistical, modeling, and computer science tools to better understand the mechanisms through which a given chemical induces harm and, ultimately, to be able to predict adverse effects of the toxicants on human health and/or the environment. The participants stressed the importance of computational toxicology to the future of environmental health sciences and regulatory decisions in public health; however, many challenges remain to be addressed before the findings from high-throughput screens and in silico models may be considered sufficiently robust and informative.

CONCLUSIONS

Many scientists, regulators, and the general public believe that new and better ways to assess human toxicity are now needed, and technological breakthroughs are empowering the field of toxicity assessment. Even though the application of computational toxicology to environmental health decisions requires additional efforts, the merger of the power of computers with biological information is poised to deliver new tools and knowledge.

The future of toxicity testing

In 2007, the U.S. National Research Council (NRC) released a report, "Toxicity Testing in the 21st Century: A Vision and a Strategy," that proposes a paradigm shift for toxicity testing of environmental agents. The vision is based on the notion that exposure to environmental agents leads to adverse health outcomes through the perturbation of toxicity pathways that are operative in humans. Implementation of the NRC vision will involve a fundamental change in the assessment of toxicity of environmental agents, moving away from adverse health outcomes observed in experimental animals to the identification of critical perturbations of toxicity pathways. Pathway perturbations will be identified using in vitro assays and quantified for dose response using methods in computational toxicology and other recent scientific advances in basic biology. Implementation of the NRC vision will require a major research effort, not unlike that required to successfully map the human genome, extending over 10 to 20 years, involving the broad scientific community to map important toxicity pathways operative in humans. This article provides an overview of the scientific tools and technologies that will form the core of the NRC vision for toxicity testing. Of particular importance will be the development of rapidly performed in vitro screening assays using human cells and cell lines or human tissue surrogates to efficiently identify environmental agents producing critical pathway perturbations. In addition to the overview of the NRC vision, this study documents the reaction by a number of stakeholder groups since 2007, including the scientific, risk assessment, regulatory, and animal welfare communities.

In Silico Toxicology in Drug Discovery — Concepts Based on Three-dimensional Models

Animal testing is still compulsory worldwide, for the approval of drugs and chemicals produced in large quantities. Computer-assisted ( in silico) technologies are considered to be efficient alternatives to in vivo experiments, and are therefore endorsed by many regulatory agencies, e.g. for use in the European REACH initiative. Advantages of in silico methods include: the possible study of hypothetical compounds; their low cost; and the fact that such virtual experiments are typically based on human data, thus making the question of interspecies transferability obsolete. Since the mid-1990s, computer-based technologies have become an indispensable tool in drug discovery — used primarily to identify small molecules displaying a stereospecific and selective binding to a regulatory macromolecule. Since toxic effects are still responsible for some 20% of the late-stage failures, there is a continuing need for in silico concepts which can be used to estimate a compound's ADMET ( adsorption, distribution, metabolism, elimination, toxicity) properties — in particular, toxicity. The aim of this paper is to provide an insight into computational technologies that allow for the prediction of toxic effects triggered by pharmaceuticals. As most adverse and toxic effects are mediated by unwanted interactions with macromolecules involved in biological regulatory systems, we have focused on methodologies that are based on three-dimensional models of small molecules binding to such entities, and discuss the results at the molecular level.