Paul J. Russell

Defining Molecular Initiating Events in the Adverse Outcome Pathway Framework for Risk Assessment

Consumer and environmental safety decisions are based on exposure and hazard data, interpreted using risk assessment approaches. The adverse outcome pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine current risk assessment practices in ecotoxicology. This framework can also be applied to human toxicology and is explored on the basis of investigating the molecular initiating events (MIEs) of compounds. The precise definition of the MIE has yet to reach general acceptance. In this work we present a unified MIE definition: an MIE is the initial interaction between a molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway. Case studies are presented, and issues with current definitions are addressed. With the development of a unified MIE definition, the field can look toward defining, classifying, and characterizing more MIEs and using knowledge of the chemistry of these processes to aid AOP research and toxicity risk assessment. We also present the role of MIE research in the development of in vitro and in silico toxicology and suggest how, by using a combination of biological and chemical approaches, MIEs can be identified and characterized despite a lack of detailed reports, even for some of the most studied molecules in toxicology.

The role of chemistry in developing understanding of adverse outcome pathways and their application in risk assessment

The Adverse Outcome Pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine the current risk assessment practice. This approach shifts the risk assessment focus from traditional apical endpoints to the development of a mechanistic understanding of a chemicals effect at a molecular and cellular level. In order to obtain this level of detail, chemistry in all its disciplines has a key role to play. Measurement techniques will be important in understanding chemical characterisation, free concentration and exposure at the site of interest. Such measurements will be vital in developing structure-based toxicological alerts and informing predictive models. This paper explores the areas where chemistry will be influential in the development of AOPs.

Safety profile of Hoodia gordonii extract: rabbit prenatal developmental toxicity study.

Hoodia gordonii extract was orally administered by gavage to groups of 22 female New Zealand white rabbits from day 3-28 after mating at doses of 0 (control), 3, 6 or 12 mg/kg bodyweight/day. These doses were reached by a dose escalation phase between days 3 and 7 after mating. As well as a vehicle control group, a control group pair-fed to the high dose was also included. On day 29 after mating the females were euthanized and examined. Treatment at 6 or 12 mg/kg/day was associated with a dose-related reduction in feed intake and bodyweight gain. Feed consumption and bodyweight gain was unaffected at 3mg/kg/day. In spite of marked maternal effects at 12 mg/kg/day, reproductive indices were unaffected at all doses and there were no effects on fetal or placental weights and no morphological changes in the fetuses. The no-observed-effect level (NOEL) for developmental effects was therefore 12 mg/kg/day, and the maternal NOEL was 3mg/kg/day. At doses that caused marked maternal effects, H. gordonii extract did not affect embryonic or fetal development in a species that is considered predictive of developmental toxicity in man.

A validated bioanalytical method in mouse, rat, rabbit and human plasma for the quantification of one of the steroid glycosides found in Hoodia gordonii extract.

Hoodia gordonii extract contains steroid glycosides, fatty acids, plant sterols and polar organic material. Certain steroid glycosides show appetite suppressant activities following oral ingestion. This study describes the validation of a bioanalytical method for the quantification of one of the steroid glycosides, H.g.-12 (≈ 10% (w/w) of the extract), in mouse, rat, rabbit and human plasma. The method utilises a liquid-liquid extraction with methyl-tert-butyl ether followed by chromatographic separation on a 2.1 × 50 mm C(18) Genesis high performance liquid chromatography (HPLC) column and detection on a triple quadrupole mass spectrometer. Detection of H.g.-12 and its stable isotope internal standards is performed using positive TurboIonspray™ ionisation in multiple reaction monitoring mode. The validation procedure demonstrated assay sensitivity, linearity, accuracy, precision and selectivity over the calibration range of 0.5-150 ng/mL in human plasma (500 μL sample volume), 1.0-100 ng/mL in rat and rabbit plasma (150 μL sample volume) and 1.0-250 ng/mL in mouse plasma (150 μL sample volume) with good recoveries (≥ 77%). H.g.-12 was stable in plasma for ≥ 6 months at -20°C, for up to 4h at ambient temperature (ca22°C) and after 3 freeze-thaw cycles. Plasma extracts were stable for up to 24h at ambient temperature.

Modification without impact: a case study in clinical assay failure due to lipemia.

After obtaining his PhD in Bioorganic Chemistry from the University of Southampton in 1999, Graeme T Clark has spent the past 13 years working in the field of analytical chemistry. He has held posts of increasing responsibility in academia, biotech, large pharma and contract research where he has both implemented novel technologies and managed functions as diverse as lipidomics, small-molecule bioanalysis (Discovery to Phase IIb clinical trials), biotransformation and LC–MS/MS-based large-molecule analysis. He has published over 18 peer-reviewed articles on subjects covering lipid profiling and biomarkers, microsampling, dried blood spot bioanalysis and novel analytical solutions to peptide bioanalysis. Graeme currently leads up the Bioanalysis and Metabolite Identification group at Cyprotex focussing on in vitro and in vivo analysis. A previously validated LC–MS/MS method for the analysis of H.g.-12 (a steroid glycoside found in Hoodia gordonii) in patients failed when clinical plasma samples from the ta...

Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors.

The Ames mutagenicity assay is a long established in vitro test to measure the mutagenicity potential of a new chemical used in regulatory testing globally. One of the key computational approaches to modeling of the Ames assay relies on the formation of chemical categories based on the different electrophilic compounds that are able to react directly with DNA and form a covalent bond. Such approaches sometimes predict false positives, as not all Michael acceptors are found to be Ames-positive. The formation of such covalent bonds can be explored computationally using density functional theory transition state modeling. We have applied this approach to mutagenicity, allowing us to calculate the activation energy required for α,β-unsaturated carbonyls to react with a model system for the guanine nucleobase of DNA. These calculations have allowed us to identify that chemical compounds with activation energies greater than or equal to 25.7 kcal/mol are not able to bind directly to DNA. This allows us to reduce the false positive rate for computationally predicted mutagenicity assays. This methodology can be used to investigate other covalent-bond-forming reactions that can lead to toxicological outcomes and learn more about experimental results.

Using 2D Structural Alerts to Define Chemical Categories for Molecular Initiating Events.

Molecular initiating events (MIEs) are important concepts for in silico predictions. They can be used to link chemical characteristics to biological activity through an adverse outcome pathway (AOP). In this work, we capture chemical characteristics in 2D structural alerts, which are then used as models to predict MIEs. An automated procedure has been used to identify these alerts, and the chemical categories they define have been used to provide quantitative predictions for the activity of molecules that contain them. This has been done across a diverse group of 39 important pharmacological human targets using open source data. The alerts for each target combine into a model for that target, and these models are joined into a tool for MIE prediction with high average model performance (sensitivity = 82%, specificity = 93%, overall quality = 93%, Matthews correlation coefficient = 0.57). The result is substantially improved from our previous study (Allen, T. E. H., Goodman, J. M., Gutsell, S., and Russell, P. J. 2016. A history of the molecular initiating event. Chem. Res. Toxicol. 29, 2060-2070) for which the mean sensitivity for each target was only 58%. This tool provides the first step in an AOP-based risk assessment, linking chemical structure to toxicity endpoint.