Use of computational tools in the field of food safety.

In this article we give an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. Our results show that currently the majority of stakeholders in the field of food safety do not apply computational methods on a routine basis, mainly because of a lack of in-house expertise. Some organisations, however, are very experienced in their use and have developed specialised in-house approaches. Despite this variable situation, computational tools are widely perceived to be a useful tool to support regulatory assessments and decision making in the field of food safety. Recognized, however, is a widespread need to develop guidance documents and software tools that will promote and harmonise the use of computational methods, together with appropriate training.

[1]  Andrew P. Worth,et al.  Applicability of QSAR analysis in the evaluation of developmental and neurotoxicity effects for the assessment of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment , 2011 .

[2]  Custer Ll,et al.  The role of genetic toxicology in drug discovery and optimization. , 2008 .

[3]  A. Bailey,et al.  The use of structure-activity relationship analysis in the food contact notification program. , 2005, Regulatory toxicology and pharmacology : RTP.

[4]  Chihae Yang,et al.  Computational Toxicology Approaches at the US Food and Drug Administration a , 2009, Alternatives to laboratory animals : ATLA.

[5]  N. Kruhlak,et al.  Assessment of the health effects of chemicals in humans: I. QSAR estimation of the maximum recommended therapeutic dose (MRTD) and no effect level (NOEL) of organic chemicals based on clinical trial data. , 2004, Current drug discovery technologies.

[6]  Naomi L Kruhlak,et al.  A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database. , 2007, Regulatory toxicology and pharmacology : RTP.

[7]  A G Renwick,et al.  Structure-based thresholds of toxicological concern--guidance for application to substances present at low levels in the diet. , 2005, Toxicology and applied pharmacology.

[8]  J. Contrera,et al.  A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. , 1998, Regulatory toxicology and pharmacology : RTP.

[9]  Naomi L Kruhlak,et al.  Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. , 2007, Regulatory toxicology and pharmacology : RTP.

[10]  Paolo Mazzatorta,et al.  Integration of Structure-Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity , 2007, J. Chem. Inf. Model..

[11]  Naomi L Kruhlak,et al.  A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals. , 2007, Regulatory toxicology and pharmacology : RTP.

[12]  Paolo Mazzatorta,et al.  Integrated Computational Methods for Prediction of the Lowest Observable Adverse Effect Level of Food‐Borne Molecules , 2007 .

[13]  Weida Tong,et al.  Assessment of Prediction Confidence and Domain Extrapolation of Two Structure–Activity Relationship Models for Predicting Estrogen Receptor Binding Activity , 2004, Environmental health perspectives.

[14]  Joseph F Contrera,et al.  Improved in silico prediction of carcinogenic potency (TD50) and the risk specific dose (RSD) adjusted Threshold of Toxicological Concern (TTC) for genotoxic chemicals and pharmaceutical impurities. , 2011, Regulatory toxicology and pharmacology : RTP.

[15]  K. Sweder,et al.  The role of genetic toxicology in drug discovery and optimization. , 2008, Current drug metabolism.

[16]  N. Kruhlak,et al.  Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose. , 2004, Regulatory toxicology and pharmacology : RTP.

[17]  Vijay K Gombar,et al.  In silico approaches to predicting cancer potency for risk assessment of genotoxic impurities in drug substances. , 2010, Regulatory toxicology and pharmacology : RTP.

[18]  Elisabeth Berger,et al.  Impact of metabolic and degradation processes on the toxicological properties of residues of pesticides in food commodities , 2010 .

[19]  I. Dewhurst,et al.  Applicability of thresholds of toxicological concern in the dietary risk assessment of metabolites, degradation and reaction products of pesticides , 2010 .

[20]  C Helma,et al.  Predictive Models for Carcinogenicity and Mutagenicity: Frameworks, State-of-the-Art, and Perspectives , 2009, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.

[21]  Paolo Mazzatorta,et al.  Modeling Oral Rat Chronic Toxicity , 2008, J. Chem. Inf. Model..

[22]  A Maunz,et al.  Prediction of chemical toxicity with local support vector regression and activity-specific kernels , 2008, SAR and QSAR in environmental research.

[23]  Guidance on information requirements and chemical safety assessment , 2008 .

[24]  C L Russom,et al.  ASTER: an integration of the AQUIRE data base and the QSAR system for use in ecological risk assessments. , 1991, The Science of the total environment.

[25]  Julie Mayer,et al.  Regulatory use of computational toxicology tools and databases at the United States Food and Drug Administration's Office of Food Additive Safety , 2010, Expert opinion on drug metabolism & toxicology.

[26]  Naomi L Kruhlak,et al.  Combined Use of MC4PC, MDL-QSAR, BioEpisteme, Leadscope PDM, and Derek for Windows Software to Achieve High-Performance, High-Confidence, Mode of Action–Based Predictions of Chemical Carcinogenesis in Rodents , 2008, Toxicology mechanisms and methods.