Assessing toxicological data quality: basic principles, existing schemes and current limitations

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.

Strategies for the optimisation of in vivo experiments in accordance with the 3Rs philosophy

There are a large number of chemicals in current use for which adequate toxicity data are not available. Whilst there are clear ethical and legal obligations to obtain data from sources other than in vivo experiments wherever possible, in certain cases in vivo assays may be deemed necessary. In such circumstances, it is essential to ensure that the maximum amount of high quality data is obtained from the minimum number of animals, using the most humane procedures, in accordance with the philosophy of reduction, refinement and replacement (3Rs). The aim of this report is to provide a strategy for anyone involved in animal experimentation, for either toxicological or pharmacological purposes, as to how in vivo experiments may be optimised. The impact of generic and endpoint specific sources of variability has been highlighted in a proof-of-principle analysis considering the variation in protocols for assays for four human health endpoints (skin sensitisation, reproductive/developmental toxicity, mutagenicity and carcinogenicity). Other factors such as operator training, experimental/statistical design, use of lower species and use of combined assays are also discussed. Recommendations for optimisation of in vivo assays, in terms of the 3Rs philosophy, applied to performing tests, harvesting data and appropriate reporting are summarised as a checklist of issues to be addressed prior to undertaking such assays.

Remote monitoring of lower-limb prosthetic socket fit using wireless technologies

Accurate fitting of a lower-limb prosthetic socket is the most important factor affecting amputee satisfaction and rehabilitation. The technology is now available to allow real-time monitoring of in-service pressure distribution of prosthetic limbs. This paper proposes a remote interfacial pressure monitoring system necessary for the assessment of fit. The suitability of a wireless ZigBee network due to its relevant technical specification is investigated. The system enables remote monitoring of a prosthetic socket and its fit under different operating conditions thereby improving design, efficiency and effectiveness. The data can be used by prosthetists and may also be recorded for future training or for patient progress monitoring. This can minimize the number of iterations by getting it right first time, thereby minimizing the number of replacement prostheses.

Repeatability analysis of the Tetrahymena pyriformis population growth impairment assay

Assessments necessary to ensure the safety of both humans and the environment are challenged by the sheer number of chemicals in use today. Chemical legislation, such as REACH, aims to use alternative methods to reduce the reliance on in vivo animal testing. Consequently, databases such as the TETRATOX database, containing data from the Tetrahymena pyriformis population growth impairment assay, have been used extensively to develop computational models which aid in priority setting and initial hazard assessments. To use any toxicological data, an assessment of quality is required. One important aspect of quality is the repeatability of the assay. This study considered TETRATOX assay data for 85 structurally and mechanistically diverse compounds. The repeatability of replicate determinations was assessed and factors relating to repeatability are discussed. Despite the majority of compounds demonstrating excellent repeatability, it was found that the mechanism of action is likely to be a modulating factor, with compounds acting via electrophilic mechanisms being more likely to exhibit reduced repeatability than those acting via narcotic mechanisms. It is evident from this study that the TETRATOX assay is a robust and highly repeatable assay, suitable for use in toxicological modelling studies and priority setting.

Formation of mechanistic categories and local models to facilitate the prediction of toxicity

There is a range of in silico techniques that can be applied to predict the toxicity of chemicals. This paper discusses the use of methods to create "local" models, particularly based around category formation and read-across, to predict toxicity. Specifically, this is illustrated with regard to categories for predicting skin sensitisation and teratogenicity. These were formed using mechanistic and structural similarity techniques to group chemicals. Local QSAR models based on grouping chemicals have the advantage that they are transparent, simple and mechanistically derived. In addition, there are a number of freely available software tools to assist in their derivation. The disadvantages include that they are labour-intensive to develop and restricted to local areas of chemistry.

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.

Data Quality Assessment for In Silico Methods: A Survey of Approaches and Needs

As indicated in Chapter 3, there are a large number of potential sources of data now available for modelling purposes. These range from historical literature references for a few compounds to highly curated databases of hundreds of thousands of compounds, available via the internet. Before including any data in an in silico model, the question of data quality must be addressed. Although it is difficult to define the quality of data in absolute terms, it is possible to assess the suitability of data for a given purpose. There are many reasons for variability within data and the degree of error that is acceptable for one model may not be the same as for another. For example generating a global model intended to pre-screen large numbers of compounds does not require the same degree of accuracy as performing an individual risk assessment for a chemical of interest. In this chapter, sources of data variability and error will be discussed and formal methods to score data quality, such as use of the Klimisch criteria, will be described. Examples of data quality issues will be given for specific endpoints relating to both environmental and human health effects. Mathematical approaches (Dempster-Schafer theory and Bayesian networks) demonstrating how this information relating to confidence in the data can be incorporated into in silico models is also discussed.

QSAR modelling of bioconcentration factor using hydrophobicity, hydrogen bonding and topological descriptors

Bioconcentration factor (BCF) is an important step in the uptake of environmental pollutants in the food chain. It is expensive and time-consuming to measure, so predictive methods are of value. We have used an artificial neural network QSAR approach involving descriptors for hydrophobicity, hydrogen bonding and molecular topology, obtained from commercially available software, to predict the fish BCF values of a diverse data set of 624 chemicals. The training set statistics were: r²= 0.765, q²= 0.763, s = 0.610, and those of the external test set were: r²= 0.739, s = 0.627. The model complies with the OECD Principles for the Validation of (Q)SARs.

The use of Mechanisms and Modes of Toxic Action in Integrated Testing Strategies: The Report and Recommendations of a Workshop held as part of the European Union OSIRIS Integrated Project

This report on The Potential of Mode of Action (MoA) Information Derived from Non-testing and Screening Methodologies to Support Informed Hazard Assessment, resulted from a workshop organised within OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information), a project partly funded by the EU Commission within the Sixth Framework Programme. The workshop was held in Liverpool, UK, on 30 October 2008, with 35 attendees. The goal of the OSIRIS project is to develop integrated testing strategies (ITS) fit for use in the REACH system, that would enable a significant increase in the use of non-testing information for regulatory decision making, and thus minimise the need for animal testing. One way to improve the evaluation of chemicals may be through categorisation by way of mechanisms or modes of toxic action. Defining such groups can enhance read-across possibilities and priority settings for certain toxic modes or chemical structures responsible for these toxic modes. Overall, this may result in a reduction of in vivo testing on organisms, through combining available data on mode of action and a focus on the potentially most-toxic groups. In this report, the possibilities of a mechanistic approach to assist in and guide ITS are explored, and the differences between human health and environmental areas are summarised.