Metabolic activation of chemicals: in-silico simulation

The VEGAHUB Platform: The Philosophy and the Tools

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

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.

Developing the Applicability Domain of In Silico Models: Relevance, Importance and Methods

The past two decades has seen the rapid growth in the development and utilisation of computational technologies to predict the toxicity of chemicals. Most notably, widespread pressure to both reduce and replace current animal testing regimes has led to in silico modelling becoming a widely utilised tool in toxicological screening. Unfortunately, given that computational models are open to misuse, there has been, and still is, significant reluctance to accept them for regulatory use. In an effort to combat this, the validation of both model and predictions is now at the forefront of research, with the concept of applicability domain being central to the validation process.

In this chapter the applicability domain concept is defined and numerous methods for its characterisation are detailed and explored with the aid of a case study example. These approaches are shown to span from relatively simple descriptor-based methods to more complex approaches based upon structural similarity or mechanism of action. Given the wealth of differing approaches available and the different information each method yields about the model, a stepwise scheme which considers numerous methods is recommended. With appreciation of model architecture and subsequent utilisation, this chapter shows that a robust and multifaceted applicability domain can be generated. Once defined, the applicability domain serves as a critical screening stage ensuring that a model is fit-for-purpose and predictions are made with maximal confidence.

Tissue-level modeling of xenobiotic metabolism in liver: An emerging tool for enabling clinical translational research

This review summarizes some of the recent developments and identifies critical challenges associated with in vitro and in silico representations of the liver and assesses the translational potential of these models in the quest of rationalizing the process of evaluating drug efficacy and toxicity. It discusses a wide range of research efforts that have produced, during recent years, quantitative descriptions and conceptual as well as computational models of hepatic processes such as biotransport and biotransformation, intra- and intercellular signal transduction, detoxification, etc. The above mentioned research efforts cover multiple scales of biological organization, from molecule-molecule interactions to reaction network and cellular and histological dynamics, and have resulted in a rapidly evolving knowledge base for a "systems biology of the liver." Virtual organ/organism formulations represent integrative implementations of particular elements of this knowledge base, usually oriented toward the study of specific biological endpoints, and provide frameworks for translating the systems biology concepts into computational tools for quantitative prediction of responses to stressors and hypothesis generation for experimental design.

Identification of transformation products of organic contaminants in natural waters by computer-aided prediction and high-resolution mass spectrometry

Transformation products (TPs) of organic contaminants in aquatic environments are still rarely considered in water quality and chemical risk assessment, although they have been found in concentrations that are of concern. Since many different TPs can potentially be formed in the environment and analytical standards are typically lacking for these compounds, knowledge on the prevalence of TPs in aquatic environments is fragmentary. In this study, an efficient procedure was therefore developed to comprehensively screen for large numbers of potential TPs in environmental samples. It is based on a target list of plausible TPs that has been assembled using the University of Minnesota Pathway Prediction System (UM-PPS) for the computer-aided prediction of products of microbial metabolism and an extensive search for TPs reported in the scientific literature. The analytical procedure for screening of the compounds on the target list has been developed to allow for the detection of a broad range of compounds in complex environmental samples in the absence of commercially available reference standards. It includes solid phase extraction with broad enrichment efficiency, followed by liquid chromatography and tandem mass spectrometry with high mass resolution and accuracy. The identification of target TPs consisted of extracting the exact mass from the chromatogram, selecting peaks of sufficient intensity, checking the plausibility of the retention time, and interpreting mass spectra. The procedure was used to screen for TPs of 52 pesticides, biocides, and pharmaceuticals in seven representative surface water samples from different regions in Switzerland. Altogether, 19 TPs were identified, including both some well-known and commonly detected TPs, and some rarely reported ones (e.g., biotransformation products of the pharmaceuticals venlafaxine and verapamil, or of the pesticide azoxystrobin). Overall, the rather low number of TPs detected suggests that TPs may not pose a problem of unexpected magnitude for aquatic resources.

Influence of structural and functional modifications of selected genotoxic carcinogens on metabolism and mutagenicity - a review

Alterations in molecular structure are responsible for the differential biological response(s) of a chemical inside a biosystem. Structural and functional parameters that govern a chemical's metabolic course and determine its ultimate outcome in terms of mutagenic/carcinogenic potential are extensively reviewed here. A large number of environmentally-significant organic chemicals are addressed under one or more broadly classified groups each representing one or more characteristic structural feature. Numerous examples are cited to illustrate the influence of key structural and functional parameters on the metabolism and DNA adduction properties of different chemicals. It is hoped that, in the event of limited experimental data on a chemical's bioactivity, such knowledge of the likely roles played by key molecular features should provide preliminary information regarding its bioactivation, detoxification and/or mutagenic potential and aid the process of screening and prioritising chemicals for further testing.

Structure-based methods for the prediction of drug metabolism

There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structure-activity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.