Establishing the level of safety concern for chemicals in food without the need for toxicity testing.

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.

Integrated in silico approaches for the prediction of Ames test mutagenicity.

The bacterial reverse mutation assay (Ames test) is a biological assay used to assess the mutagenic potential of chemical compounds. In this paper approaches for the development of an in silico mutagenicity screening tool are described. Three individual in silico models, which cover both structure activity relationship methods (SARs) and quantitative structure activity relationship methods (QSARs), were built using three different modelling techniques: (1) an in-house alert model: which uses SAR approach where alerts are generated based on experts judgements; (2) a kNN approach (k-Nearest Neighbours), which is a QSAR model where a prediction is given based on outcomes of its k chemical neighbours; (3) a naive Bayesian model (NB), which is another QSAR model, where a prediction is derived using a Bayesian formula through preselected identified informative chemical features (e.g., physico-chemical, structural descriptors). These in silico models, were compared against two well-known alert models (DEREK and ToxTree) and also against three different consensus approaches (Categorical Bayesian Integration Approach (CBI), Partial Least Squares Discriminate Analysis (PLS-DA) and simple majority vote approach). By applying these integration methods on the validation sets it was shown that both integration models (PLS-DA and CBI) achieved better performance than any of the individual models or consensus obtained by simple majority rule. In conclusion, the recommendation of this paper is that when obtaining consensus predictions for Ames mutagenicity, approaches like PLS-DA or CBI should be the first choice for the integration as compared to a simple majority vote approach.

Comparison of the predictive nature of the Genomic Allergen Rapid Detection (GARD) assay with mammalian assays in determining the skin sensitisation potential of agrochemical active ingredients.

Alternatives to mammalian testing are highly desirable to predict the skin sensitisation potential of agrochemical active ingredients (AI). The GARD assay, a stimulated, dendritic cell-like, cell line measuring genomic signatures, was evaluated using twelve AIs (seven sensitisers and five non-sensitisers) and the results compared with historical results from guinea pig or local lymph node assay (LLNA) studies. Initial GARD results suggested 11/12 AIs were sensitisers and six concurred with mammalian data. Conformal predictions changed one AI to a non-sensitiser. An AI identified as non-sensitising in the GARD assay was considered a potent sensitiser in the LLNA. In total 7/12 GARD results corresponded with mammalian data. AI chemistries might not be comparable to the GARD training set in terms of applicability domains. Whilst the GARD assay can replace mammalian tests for skin sensitisation evaluation for compounds including cosmetic ingredients, further work in agrochemical chemistries is needed for this assay to be a viable replacement to animal testing. The work conducted here is, however, considered exploratory research and the methodology needs further development to be validated for agrochemicals. Mammalian and other alternative assays for regulatory safety assessments of AIs must provide confidence to assign the appropriate classification for human health protection.

ADMET rules of thumb II: A comparison of the effects of common substituents on a range of ADMET parameters.

A systematic analysis of data generated in key in vitro assays within GSK has been undertaken to identify what impact a range of common substituents have on a range of ADMET parameters. These include; P450 1A2, 2C9, 2C19, 2D6 and 3A4 inhibition, hERG inhibition, phosphate buffer solubility and artificial membrane permeability. We do this by identifying all matched molecular pairs, differing by the replacement of a hydrogen atom with a list of predefined substituents. For each substituent we calculate the mean difference in the ADMET parameter for all the matched molecular pairs identified, making a statistical assessment of the difference, as well as assessing the diversity for each example to ensure that the results can be generalized. We also relate the change in activity observed for each substituent to differences in their molecular properties in an effort to identify any structural alerts.

An Unusual Transorbital Penetrating Injury by House-key (lock): A Case Report with a Small Review of Literature.

Penetrating injuries of the brain are quite uncommon, comprising approximately 0.4% of all head injuries. In our case, a four-year-old boy who fell forward on a house-key (lock) accidentally while playing with some other children sustained a left sided penetrating transorbital brain injury. After hospital admission, the patient had a Glasgow Coma Scale (GCS) score of 15/15, no visual loss but restriction of upward gaze (left eye) and profuse bleeding from the wound site. Firstly, the metallic key was removed in emergency operation theatre and haemostasis secured. Next day we did a combined surgical approach with neurosurgeons, Eye-surgeons and general surgeons after having CT scan report. We report this case because penetrating head injury is rare and transorbital penetrating head injury is even rarer and a predicament in emergency surgical practice with controversial management.

The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries.

In silico toxicology prediction is an extremely challenging area because many toxicological effects are a result of changes in multiple physiological processes. In this article we discuss limitations and strengths of these in silico tools. Additionally, we look at different parameters that are necessary to make the best use of these tools, and also how to gain acceptance outside the modelling community and into the regulatory arena. As a solution, we propose an integrated workflow for combined use of data extraction, quantitative structure activity relationships and read-across methods. We also discuss how the recent advances in this field can enable transition to a new paradigm of the discovery process, as exemplified by the Toxicity Testing in the 21st Century initiative.

Reprint of: Characterising hepatic mitochondrial function as a model for systemic toxicity: a commentary.

A major challenge in toxicology is the development of non-animal methods for the assessment of human health risks that might result from repeated systemic exposure. We present here a perspective that considers the opportunities that computational modelling methods may offer in addressing this challenge. Our approach takes the form of a commentary designed to inform responses to future calls for research in predictive toxicology. It is considered essential that computational model-building activities be at the centre of the initiative, driving an iterative process of development, testing and refinement. It is critical that the models provide mechanistic understanding and quantitative predictions. The aim would be to predict effects in humans; in order to help define a challenging but yet feasible initial goal the focus would be on liver mitochondrial toxicity. This will inevitably present many challenges that naturally lead to a modular approach, in which the overall problem is broken down into smaller, more self-contained sub-problems that will subsequently need to be connected and aligned to develop an overall understanding. The project would investigate multiple modelling approaches in order to encourage links between the various disciplines that hitherto have often operated in isolation. The project should build upon current activities in the wider scientific community, to avoid duplication of effort and to ensure that investment is maximised. Strong leadership will be required to ensure alignment around a set of common goals that would be derived using a problem-statement driven approach. Finally, although the focus here is on toxicology, there is a clear link to the wider challenges in systems medicine and improving human health.

The challenges involved in modeling toxicity data in silico: a review.

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

Crystal structure of human cytochrome P450 2D6.

Cytochrome P450 2D6 is a heme-containing enzyme that is responsible for the metabolism of at least 20% of known drugs. Substrates of 2D6 typically contain a basic nitrogen and a planar aromatic ring. The crystal structure of human 2D6 has been solved and refined to 3.0A resolution. The structure shows the characteristic P450 fold as seen in other members of the family, with the lengths and orientations of the individual secondary structural elements being very similar to those seen in 2C9. There are, however, several important differences, the most notable involving the F helix, the F-G loop, the B'helix, beta sheet 4, and part of beta sheet 1, all of which are situated on the distal face of the protein. The 2D6 structure has a well defined active site cavity above the heme group, containing many important residues that have been implicated in substrate recognition and binding, including Asp-301, Glu-216, Phe-483, and Phe-120. The crystal structure helps to explain how Asp-301, Glu-216, and Phe-483 can act as substrate binding residues and suggests that the role of Phe-120 is to control the orientation of the aromatic ring found in most substrates with respect to the heme. The structure has been compared with published homology models and has been used to explain much of the reported site-directed mutagenesis data and help understand the metabolism of several compounds.

Structure-activity relationship for human cytochrome P450 substrates and inhibitors.

Criteria governing the avidity of substrate binding to human hepatic cytochromes P450 (CYP) associated with Phase 1 metabolism of drugs are described. The results of extensive quantitative structure-activity relationship (QSAR) analyses are reported for substrates of human P450s: CYPIA2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4, representing the enzymes exhibiting major involvement in the metabolism of drug substrates in Homo sapiens. In particular, it is shown that hydrogen bond properties in each class of enzyme-substrate complex are especially important factors in determining substrate binding affinity towards those human P450s which are involved in drug metabolism.