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Jane is science lead and responsible for the management of Fera’s In Silico Predictive Toxicology (INSPECT) team. Her role is to deliver a high-quality service to a wide range of UK and overseas government and commercial clients.
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Area of expertise
Proficiency Testing
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Years of experience
20+
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Highest qualification
PhD
Jane is science lead and responsible for the management of Fera’s In Silico Predictive Toxicology (INSPECT) team. Her role is to deliver a high-quality service to a wide range of UK and overseas government and commercial clients. Estimates for a range of (eco)toxicological endpoints are used by INSPECT clients for regulatory submissions, screening and prioritisation, or to identify potential toxicity concerns for products at an early stage of development. The team utilises a range of in silico techniques, including Quantitative Structure Activity Relationships (QSAR), Read-across and Expert systems using a Weight of Evidence approach to obtain reliable (eco)toxicological estimates where experimental data is not available.
Jane has participated in several EU projects aimed at developing QSAR models designed specifically to be acceptable for regulatory use, including applying in silico models as part of a mixture test strategy in the Euromix project (https://www.eromixproject.eu).
More about In-Silico Predictive Toxicology
In Silico Predictive Toxicology (INSPECT) provides you with a reliable, rapid, ethical and cost-effective way to assess the safety of chemical products. Advanced computational (in silico) modelling means that we can now rapidly derive reliable (eco)toxicological assessments without the need for testing on animals.
Writing
Services
Latest Papers and Articles
Cotterill, J.V., Palazzolo, L., Ridgway, C., Price, N. , Rorije, E., Moretto, A., Peijnenburg, A. and Eberini, I. (2019). Predicting Estrogen receptor binding of chemicals using a suite of in silico methods – complementary approaches of (Q)SAR, Molecular Docking and Molecular Dynamics. Toxicology and Applied Pharmacology, 378: 114630
Nowack, B., Baalousha, M., Bornhöft, N., Chaudhry, Q, Cornelis, G., Cotterill, J., Gondikas, A., Hassellöv, M., Lead, J., Mitrano, D.M., von der Kammer, F. and Wontner-Smith, T. 2015. Progress towards the validation of modeled environmental concentrations of engineered nanomaterials by analytical measurements. Environ. Sci. Nano, 2: 421-428.
Chaudhry, Q., Piclin, N., Cotterill, J., Pinatore, M., Price, N.R., Chretien, J.R. and Roncaglioni, A. (2010) Global QSAR models of skin sensitisers for regulatory purposes. Chemistry Central Journal, 4(Suppl 1):S5
Cotterill, JV; Chaudhry, MQ; Matthews, W; Watkins, RW (2008). In silico assessment of toxicity of heat-generated food contaminants. Food & Chemical Toxicology 46: 1905-1918
Cotterill, J.V., Price, N., Rorije, E., and Peijnenburg, A. 2020. Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food and Chemical Toxicology, 142: 111494
Jane and their team work with customers to deliver the following services within Fera...
Food Contact Materials Testing
Our work involves targeted and non targeted analysis of known and unknown migrants. Analysis can be carried out in complex matrices (foods, food simulants and food contact materials and articles) utilising a range of advanced chromatographic and mass spectrometric techniques.
Contaminant Testing
Our experts focus on the scientific research relating to intentionally added food components and to those arising from chemical changes during food processing, bioactive chemicals and natural toxicants.
Authenticity Testing
Fera's analysts use industry leading techniques and cutting edge technologies to ensure compliance with food safety regulations, protecting public health and traceability.
Writing
Latest Papers and Articles
Cotterill, J.V., Palazzolo, L., Ridgway, C., Price, N. , Rorije, E., Moretto, A., Peijnenburg, A. and Eberini, I. (2019). Predicting Estrogen receptor binding of chemicals using a suite of in silico methods – complementary approaches of (Q)SAR, Molecular Docking and Molecular Dynamics. Toxicology and Applied Pharmacology, 378: 114630
Nowack, B., Baalousha, M., Bornhöft, N., Chaudhry, Q, Cornelis, G., Cotterill, J., Gondikas, A., Hassellöv, M., Lead, J., Mitrano, D.M., von der Kammer, F. and Wontner-Smith, T. 2015. Progress towards the validation of modeled environmental concentrations of engineered nanomaterials by analytical measurements. Environ. Sci. Nano, 2: 421-428.
Chaudhry, Q., Piclin, N., Cotterill, J., Pinatore, M., Price, N.R., Chretien, J.R. and Roncaglioni, A. (2010) Global QSAR models of skin sensitisers for regulatory purposes. Chemistry Central Journal, 4(Suppl 1):S5
Cotterill, JV; Chaudhry, MQ; Matthews, W; Watkins, RW (2008). In silico assessment of toxicity of heat-generated food contaminants. Food & Chemical Toxicology 46: 1905-1918
Cotterill, J.V., Price, N., Rorije, E., and Peijnenburg, A. 2020. Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools. Food and Chemical Toxicology, 142: 111494
Services
Jane and their team work with customers to deliver the following services within Fera...
Food Contact Materials Testing
Our work involves targeted and non targeted analysis of known and unknown migrants. Analysis can be carried out in complex matrices (foods, food simulants and food contact materials and articles) utilising a range of advanced chromatographic and mass spectrometric techniques.
Contaminant Testing
Our experts focus on the scientific research relating to intentionally added food components and to those arising from chemical changes during food processing, bioactive chemicals and natural toxicants.
Authenticity Testing
Fera's analysts use industry leading techniques and cutting edge technologies to ensure compliance with food safety regulations, protecting public health and traceability.
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For further information about how Fera uses any personal data collected from you, please see our Privacy Notice at www.fera.co.uk/privacy-policy.