INTERPRETATION OF QSAR MODELS: THE IMPORTANCE OF MOLECULAR CONTEXT IN MINING STRUCTURAL PATTERNS

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In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This study has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, were developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given

Read-across of 90-Day Rat Oral Repeated-Dose Toxicity: A Case Study for Selected 2-Alkyl-1-alkanols

2-Alkyl-1-alkanols offer an example whereby the category approach to read-across can be used to predict repeated-dose toxicity for a variety of derivatives. Specifically, the NOAELs of 125 mg/kg bw/d for 2-ethyl-1-hexanol and 2-propyl-1-heptanol, the source substances, can be read across with confidence to untested 2-alkyl-1-alkanols in the C5 to C13 category based on a LOAEL of low systemic toxicity. These branched alcohols, while non-reactive and exhibiting unspecific, reversible simple anaesthesia or nonpolar narcosis mode of toxic action, have metabolic pathways that have significance to repeated-dose toxic potency. In this case study, the chemical category is limited to the readily bioavailable analogues. The read-across premise includes rapid absorption via the gastrointestinal tract, distribution in the circulatory system and first-pass metabolism in the liver via Phase 2 glucuronidation prior to urinary elimination. 2-Ethyl-1-hexanol and 2-propyl-1-heptanol, the source substances, have high quality 90-day oral repeated-dose toxicity studies (OECD TG 408) that exhibit qualitative and quantitative consistency. Findings include only mild changes consistent with low-grade effects including decreased body weight and slightly increased liver weight, which in some cases is accompanied by clinical chemical and haematological changes but generally without concurrent histopathological effects at the LOAEL. These findings are supported by results from the TG 408 assessment of a semi-defined mixture of isotridecanols. Chemical similarity between the analogues is readily defined and data uncertainty associated with toxicokinetic and toxicodynamics similarities are low. Uncertainty associated with mechanistic relevance and completeness of the read-across is reduced by the concordance of in vivo and in vitro results, as well as high throughput and in silico methods data. As shown in detail, the 90-day rat oral repeated-dose NOAEL values for the two source substances can be read across to fill the data gaps of the untested analogues in this category with uncertainty deemed equivalent to results from a TG 408 assessment

A Strategy for Structuring and Reporting a Read-Across Prediction of Toxicity

Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be used to fill data gaps for regulatory decisions is proposed. There are two major aspects of any read-across exercise, namely assessing similarity and uncertainty. While there can be an over-arching rationale for grouping organic substances based on molecular structure and chemical properties, these similarities alone are generally not sufficient to justify a read-across prediction. Further scientific justification is normally required to justify the chemical grouping, typically including considerations of bioavailability, metabolism and biological/mechanistic plausibility. Sources of uncertainty include a variety of elements which are typically divided into two main issues: the uncertainty associated firstly with the similarity justification and secondly the completeness of the read-across argument. This article focuses on chronic toxicity, whilst acknowledging the approaches are applicable to all endpoints. Templates, developed from work to prepare for the application of new toxicological data to read-across assessment, are presented. These templates act as proposals to assist in assessing similarity in the 50 context of chemistry, toxicokinetics and toxicodynamics as well as to guide the systematic characterisation of uncertainty both in the context of the similarity rationale, the read across data and overall approach and conclusion. Lastly, a workflow for reporting a read-across prediction is suggested

A critical review of adverse effects to the kidney: mechanisms, data sources and in silico tools to assist prediction

Introduction: The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilise multi-scale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo and human data that may assist in the development of in silico models which in turn may shed light on the inter-relationships between nephrotoxicity mechanisms

Molecular Fingerprint-Derived Similarity Measures for Toxicological Read-Across: Recommendations for Optimal Use

Computational approaches are increasingly used to predict toxicity, in part due to pressures to find alternatives to animal testing. Read-across is the “new paradigm” which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities, i.e. molecular similarity is integral to read-across. Various molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

Introduction: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed

INTERPRETATION OF QSAR MODELS: THE IMPORTANCE OF MOLECULAR CONTEXT IN MINING STRUCTURAL PATTERNS

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