12 Application of Computational Methods for the Safety Assessment …
253
Disclaimer The views and data interpretations expressed in this chapter represent that of the authors
and not necessarily of the US FDA. The use of trade names, company names, product names, etc.,
is for example/illustrative purposes only and should not be construed as endorsements by the FDA
or the authors.
References
1. CFSAN-FDA. Redbook: guidance for industry and other stakeholders. Toxicological principles
for the safety assessment of food ingredients, College Park, MD: CFSAN, US FDA, DHHS,
1993 Revised July 2007. Report No. https://www.fda.gov/downloads/food/guidanceregulation/
ucm222779.pdf
2. FDA (2007) Guidance for industry: preparation of premarket submissions for
food contact substances (chemistry recommendations), College Park, MD. https://
www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/
IngredientsAdditivesGRASPackaging/ucm081818.htm
3. FDA (2009) Guidance for industry: recommendations for submission of chemical and technological data for direct food additive petitions, College Park, MD. https://www.fda.gov/Food/
GuidanceRegulation/ucm124917.htm
4. FDA (2002) Guidance for industry: preparation of food contact notifications for food contact substances (toxicology recommendations). DFCN/OFAS/CFSAN, College Park, MD.
https://www.fda.gov/food/guidanceregulation/guidancedocumentsregulatoryinformation/
ingredientsadditivesgraspackaging/ucm081825.htm
5. FDA (2017) FDA’s predictive toxicology roadmap. https://www.fda.gov/downloads/
scienceresearch/specialtopics/regulatoryscience/ucm587831.pdf
6. Workgroup EM, Marshall SF, Burghaus R, Cosson V, Cheung SY, Chenel M, DellaPasqua
O, Frey N, Hamren B, Harnisch L, Ivanow F, Kerbusch T, Lippert J, Milligan PA, Rohou S,
Staab A, Steimer JL, Tornoe C, Visser SA (2016) Good practices in model-informed drug
discovery and development: practice, application, and documentation. CPT Pharmacometrics
Syst Pharmacol 5(3):93–122
7. Roy K, Kar S, Das RN (2015) Chemical information and descriptors. Understanding the basics
of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press,
Tokyo
8. Danishuddin, Khan AU (2016) Descriptors and their selection methods in QSAR analysis:
paradigm for drug design. Drug Discov Today 21(8):1291–1302
9. Parthasarathi R, Dhawan A (2018) In silico approaches for predictive toxicology. In: Dhawan
AK, Kwon S (eds) In vitro toxicology. Academic Press, pp 91–109
10. Arvidson KB, Chanderbhan R, Muldoon-Jacobs K, Mayer J, Ogungbesan A (2010) Regulatory
use of computational toxicology tools and databases at the United States Food and Drug Administration’s Office of Food Additive Safety. Expert Opin Drug Metab Toxicol 6(7):793–796
11. FDA (2018) FDA Cooperative Research and Development Agreements (CRADAs). https://
www.fda.gov/ScienceResearch/CollaborativeOpportunities/CooperativeResearchandDevelopmentAgreementsCRADAs/ucm122820.htm (cited 2018 06/06)
12. Leadscope (2018) Leadscope: enterprise and model applier. http://www.leadscope.com/ (cited
2018 06/06)
13. Lhasa Limited (2018) Lhasa Limited: Derek Nexus. https://www.lhasalimited.org/products/
derek-nexus.htm (cited 2018 06/06)
14. Lhasa Limited (2018) Lhasa Limited: Vitic Nexus. https://www.lhasalimited.org/products/
vitic-nexus.htm (cited 2018 06/06)
15. MultiCASE (2017) MultiCASE. http://www.multicase.com/ (04/07/2018)
16. Valencia A, Prous J, Mora O, Sadrieh N, Valerio LG Jr (2013) A novel QSAR model of
Salmonella mutagenicity and its application in the safety assessment of drug impurities. Toxicol
Appl Pharmacol 273(3):427–434
253
Disclaimer The views and data interpretations expressed in this chapter represent that of the authors
and not necessarily of the US FDA. The use of trade names, company names, product names, etc.,
is for example/illustrative purposes only and should not be construed as endorsements by the FDA
or the authors.
References
1. CFSAN-FDA. Redbook: guidance for industry and other stakeholders. Toxicological principles
for the safety assessment of food ingredients, College Park, MD: CFSAN, US FDA, DHHS,
1993 Revised July 2007. Report No. https://www.fda.gov/downloads/food/guidanceregulation/
ucm222779.pdf
2. FDA (2007) Guidance for industry: preparation of premarket submissions for
food contact substances (chemistry recommendations), College Park, MD. https://
www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/
IngredientsAdditivesGRASPackaging/ucm081818.htm
3. FDA (2009) Guidance for industry: recommendations for submission of chemical and technological data for direct food additive petitions, College Park, MD. https://www.fda.gov/Food/
GuidanceRegulation/ucm124917.htm
4. FDA (2002) Guidance for industry: preparation of food contact notifications for food contact substances (toxicology recommendations). DFCN/OFAS/CFSAN, College Park, MD.
https://www.fda.gov/food/guidanceregulation/guidancedocumentsregulatoryinformation/
ingredientsadditivesgraspackaging/ucm081825.htm
5. FDA (2017) FDA’s predictive toxicology roadmap. https://www.fda.gov/downloads/
scienceresearch/specialtopics/regulatoryscience/ucm587831.pdf
6. Workgroup EM, Marshall SF, Burghaus R, Cosson V, Cheung SY, Chenel M, DellaPasqua
O, Frey N, Hamren B, Harnisch L, Ivanow F, Kerbusch T, Lippert J, Milligan PA, Rohou S,
Staab A, Steimer JL, Tornoe C, Visser SA (2016) Good practices in model-informed drug
discovery and development: practice, application, and documentation. CPT Pharmacometrics
Syst Pharmacol 5(3):93–122
7. Roy K, Kar S, Das RN (2015) Chemical information and descriptors. Understanding the basics
of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press,
Tokyo
8. Danishuddin, Khan AU (2016) Descriptors and their selection methods in QSAR analysis:
paradigm for drug design. Drug Discov Today 21(8):1291–1302
9. Parthasarathi R, Dhawan A (2018) In silico approaches for predictive toxicology. In: Dhawan
AK, Kwon S (eds) In vitro toxicology. Academic Press, pp 91–109
10. Arvidson KB, Chanderbhan R, Muldoon-Jacobs K, Mayer J, Ogungbesan A (2010) Regulatory
use of computational toxicology tools and databases at the United States Food and Drug Administration’s Office of Food Additive Safety. Expert Opin Drug Metab Toxicol 6(7):793–796
11. FDA (2018) FDA Cooperative Research and Development Agreements (CRADAs). https://
www.fda.gov/ScienceResearch/CollaborativeOpportunities/CooperativeResearchandDevelopmentAgreementsCRADAs/ucm122820.htm (cited 2018 06/06)
12. Leadscope (2018) Leadscope: enterprise and model applier. http://www.leadscope.com/ (cited
2018 06/06)
13. Lhasa Limited (2018) Lhasa Limited: Derek Nexus. https://www.lhasalimited.org/products/
derek-nexus.htm (cited 2018 06/06)
14. Lhasa Limited (2018) Lhasa Limited: Vitic Nexus. https://www.lhasalimited.org/products/
vitic-nexus.htm (cited 2018 06/06)
15. MultiCASE (2017) MultiCASE. http://www.multicase.com/ (04/07/2018)
16. Valencia A, Prous J, Mora O, Sadrieh N, Valerio LG Jr (2013) A novel QSAR model of
Salmonella mutagenicity and its application in the safety assessment of drug impurities. Toxicol
Appl Pharmacol 273(3):427–434