1 Computational Toxicology Promotes Regulatory Science
9
cussed the implementations of workflows for assessing trusted reproducible in silico
evidence supported within OpenTox and OpenRiskNet.
1.6 Conclusions
Translating computational approaches into the complex and intricate field of toxicity
is not a simple task. Working together as a community across countries, disciplines,
and organizations, computational toxicology was born and has been taking flight. As
the field advances, open communication must continue across international borders
as well as between regulators and researchers to continually move toward more
efficient and effective toxicology.
Disclaimer This article reflects the views of the authors and should not be construed to represent
the FDA’s views or policies.
References
1. Wennig R (2009) Back to the roots of modern analytical toxicology: Jean Servais Stas and the
Bocarmé murder case. Drug Test Anal 1(4):153–155
2. de Bruin YB, Eskes C, Langezaal I, Coecke S, Kinsner-Ovaskainen A, Hakkinen PJ (2009)
In: Wexler P, Hakkinen PJ, Mohapatra A, and Gilbert SG (eds) Testing methods and toxicity
assessment (including alternatives), in information resources in toxicology, 4th edn. Elsevier,
pp 497–513
3. Kolar R (1998) OECD pushes antiquated animal test for endocrine disrupting activity. Altex
15(4):215–217
4. Bruner LH (1992) Alternatives to the use of animals in household product and cosmetic testing.
J Am Vet Med Assoc 200(5):669–673
5. Indans I (2002) The use and interpretation of in vitro data in regulatory toxicology: cosmetics,
toiletries and household products. Toxicol Lett 127(1–3):177–182
6. McNamee P1, Hibatallah J, Costabel-Farkas M, Goebel C, Araki D, Dufour E, Hewitt NJ, Jones
P, Kirst A, Le Varlet B, Macfarlane M, Marrec-Fairley M, Rowland J, Schellauf F, Scheel J.
(2009) A tiered approach to the use of alternatives to animal testing for the safety assessment
of cosmetics: eye irritation. Regul Toxicol Pharmacol 54(2):197–209
7. Reisfeld B1, Mayeno AN (2012) What is computational toxicology? Methods Mol Biol 929:3–7
8. Kavlock RJ, Ankley G, Blancato J, Breen M, Conolly R, Dix D, Houck K, Hubal E, Judson
R, Rabinowitz J, Richard A, Setzer RW, Shah I, Villeneuve D, Weber E (2008) Computational
toxicology—a state of the science mini review. Toxicol Sci 103:14–27
9. Benfenati E (2007) Predicting toxicity through computers: a changing world. Chem Central J
1:32
10. Hong H, Neamati N, Winslow HE, Christensen JL, Orr A, Pommier Y, Milne GWA (1998)
Identification of HIV-1 integrase inhibitors based on a four-point pharmacophore. Antiviral
Chem Chemother 9:461–472
11. Shen J, Zhang W, Fang H, Perkins R, Tong W, Hong H (2013) Homology modeling, molecular
docking, and molecular dynamics simulations elucidated α-fetoprotein binding modes. BMC
Bioinform 14:S6
9
cussed the implementations of workflows for assessing trusted reproducible in silico
evidence supported within OpenTox and OpenRiskNet.
1.6 Conclusions
Translating computational approaches into the complex and intricate field of toxicity
is not a simple task. Working together as a community across countries, disciplines,
and organizations, computational toxicology was born and has been taking flight. As
the field advances, open communication must continue across international borders
as well as between regulators and researchers to continually move toward more
efficient and effective toxicology.
Disclaimer This article reflects the views of the authors and should not be construed to represent
the FDA’s views or policies.
References
1. Wennig R (2009) Back to the roots of modern analytical toxicology: Jean Servais Stas and the
Bocarmé murder case. Drug Test Anal 1(4):153–155
2. de Bruin YB, Eskes C, Langezaal I, Coecke S, Kinsner-Ovaskainen A, Hakkinen PJ (2009)
In: Wexler P, Hakkinen PJ, Mohapatra A, and Gilbert SG (eds) Testing methods and toxicity
assessment (including alternatives), in information resources in toxicology, 4th edn. Elsevier,
pp 497–513
3. Kolar R (1998) OECD pushes antiquated animal test for endocrine disrupting activity. Altex
15(4):215–217
4. Bruner LH (1992) Alternatives to the use of animals in household product and cosmetic testing.
J Am Vet Med Assoc 200(5):669–673
5. Indans I (2002) The use and interpretation of in vitro data in regulatory toxicology: cosmetics,
toiletries and household products. Toxicol Lett 127(1–3):177–182
6. McNamee P1, Hibatallah J, Costabel-Farkas M, Goebel C, Araki D, Dufour E, Hewitt NJ, Jones
P, Kirst A, Le Varlet B, Macfarlane M, Marrec-Fairley M, Rowland J, Schellauf F, Scheel J.
(2009) A tiered approach to the use of alternatives to animal testing for the safety assessment
of cosmetics: eye irritation. Regul Toxicol Pharmacol 54(2):197–209
7. Reisfeld B1, Mayeno AN (2012) What is computational toxicology? Methods Mol Biol 929:3–7
8. Kavlock RJ, Ankley G, Blancato J, Breen M, Conolly R, Dix D, Houck K, Hubal E, Judson
R, Rabinowitz J, Richard A, Setzer RW, Shah I, Villeneuve D, Weber E (2008) Computational
toxicology—a state of the science mini review. Toxicol Sci 103:14–27
9. Benfenati E (2007) Predicting toxicity through computers: a changing world. Chem Central J
1:32
10. Hong H, Neamati N, Winslow HE, Christensen JL, Orr A, Pommier Y, Milne GWA (1998)
Identification of HIV-1 integrase inhibitors based on a four-point pharmacophore. Antiviral
Chem Chemother 9:461–472
11. Shen J, Zhang W, Fang H, Perkins R, Tong W, Hong H (2013) Homology modeling, molecular
docking, and molecular dynamics simulations elucidated α-fetoprotein binding modes. BMC
Bioinform 14:S6