363
11 Computational Toxicology in Drug Discovery: Opportunities and Limitations
Another important factor that influences on the accuracy of models is a metabolic pathway or metabolic activation of the parent compound which is not usually
considered. It would be reasonable to predict the first reactive metabolite or find an
actual proof that it is produced, and then make a separate prediction for the metabolite of interest. Several computer programs provide either the prediction of human
metabolism or common metabolic pathway based on the rules extracted from several species of mammals (rat, mouse, human, hamster), using a mixture of in vitro
and in vivo metabolism data of compounds [68, 89]. However, in these programs,
predictions made for the possible reactive metabolites do not affect the prediction
for a parent compound.
References
1. Valerio LG Jr (2009) In silico toxicology for the pharmaceutical sciencesâ. Toxicol Appl
Pharmacol 241:356–370. doi:10.1016/j.taap.2009.08.022
2. Snyder RD (2009) An update on the genotoxicity and carcinogenicity of marketed pharmaceuticals with reference to in silico predictivity. Environ Mol Mutagen 50:435–450.
doi:10.1002/em.20485
3. Kavlock RJ, Ankley G, Blancato J et al (2008) Computational toxicology—a state of the science mini review. Toxicol Sci Off J Soc Toxicol 103:14–27. doi:10.1093/toxsci/kfm297
4. (2007) Tools and technologies Chapter 4. Toxic. Test. 21st Century Vis. Strategy. National
Academies Press, Washington, DC, pp 98–119
5. (2006) EU. Official. J. Eur. Union. L396
6. Lahl U, Gundert-Remy U (2008) The use of (Q)SAR methods in the context of REACH.
Toxicol Mech Methods 18:149–158. doi:10.1080/15376510701857288
7. Benfenati E, Diaza RG, Cassano A et al (2011) The acceptance of in silico models for
REACH: requirements, barriers, and perspectives. Chem Cent J 5:58. doi:10.1186/1752153X-5–58
8. Ashby J (1985) Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity. Environ Mutagen 7:919–921
9. Ashby J, Tennant RW (1988) Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents
by the U.S. NCI/NTP. Mutat Res 204:17–115
10. Ashby J, Tennant RW (1991) Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. Mutat Res 257:229–306
11. OECD principles. OECD Princ. http://www.oecd.org/env/ehs/risk-assessment/37849783.
pdf. Accessed 7 May 2013
12. Gedeck P, Rohde B, Bartels C (2006) QSAR—how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets. J Chem Inf Model 46:1924–
1936. doi:10.1021/ci050413p
13. Borth DM (1996) Optimal experimental designs for (possibly) censored data. Chemom Intell
Lab Syst 32:25–35. doi:10.1016/0169-7439(95)00057-7
14. Borth DM, Wilhelm MS (2002) Confidence limits for normal type I censored regression.
Chemom Intell Lab Syst 63:117–128. doi:10.1016/S0169-7439(02)00019-9
15. Matthews EJ, Kruhlak NL, Benz RD et al (2008) 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. Toxicol Mech Methods 18:189–206. doi:10.1080/15376510701857379
16. Gottmann E, Kramer S, Pfahringer B, Helma C (2001) Data quality in predictive toxicology:
reproducibility of rodent carcinogenicity experiments. Environ Health Perspect 109:509–514
11 Computational Toxicology in Drug Discovery: Opportunities and Limitations
Another important factor that influences on the accuracy of models is a metabolic pathway or metabolic activation of the parent compound which is not usually
considered. It would be reasonable to predict the first reactive metabolite or find an
actual proof that it is produced, and then make a separate prediction for the metabolite of interest. Several computer programs provide either the prediction of human
metabolism or common metabolic pathway based on the rules extracted from several species of mammals (rat, mouse, human, hamster), using a mixture of in vitro
and in vivo metabolism data of compounds [68, 89]. However, in these programs,
predictions made for the possible reactive metabolites do not affect the prediction
for a parent compound.
References
1. Valerio LG Jr (2009) In silico toxicology for the pharmaceutical sciencesâ. Toxicol Appl
Pharmacol 241:356–370. doi:10.1016/j.taap.2009.08.022
2. Snyder RD (2009) An update on the genotoxicity and carcinogenicity of marketed pharmaceuticals with reference to in silico predictivity. Environ Mol Mutagen 50:435–450.
doi:10.1002/em.20485
3. Kavlock RJ, Ankley G, Blancato J et al (2008) Computational toxicology—a state of the science mini review. Toxicol Sci Off J Soc Toxicol 103:14–27. doi:10.1093/toxsci/kfm297
4. (2007) Tools and technologies Chapter 4. Toxic. Test. 21st Century Vis. Strategy. National
Academies Press, Washington, DC, pp 98–119
5. (2006) EU. Official. J. Eur. Union. L396
6. Lahl U, Gundert-Remy U (2008) The use of (Q)SAR methods in the context of REACH.
Toxicol Mech Methods 18:149–158. doi:10.1080/15376510701857288
7. Benfenati E, Diaza RG, Cassano A et al (2011) The acceptance of in silico models for
REACH: requirements, barriers, and perspectives. Chem Cent J 5:58. doi:10.1186/1752153X-5–58
8. Ashby J (1985) Fundamental structural alerts to potential carcinogenicity or noncarcinogenicity. Environ Mutagen 7:919–921
9. Ashby J, Tennant RW (1988) Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents
by the U.S. NCI/NTP. Mutat Res 204:17–115
10. Ashby J, Tennant RW (1991) Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. Mutat Res 257:229–306
11. OECD principles. OECD Princ. http://www.oecd.org/env/ehs/risk-assessment/37849783.
pdf. Accessed 7 May 2013
12. Gedeck P, Rohde B, Bartels C (2006) QSAR—how good is it in practice? Comparison of descriptor sets on an unbiased cross section of corporate data sets. J Chem Inf Model 46:1924–
1936. doi:10.1021/ci050413p
13. Borth DM (1996) Optimal experimental designs for (possibly) censored data. Chemom Intell
Lab Syst 32:25–35. doi:10.1016/0169-7439(95)00057-7
14. Borth DM, Wilhelm MS (2002) Confidence limits for normal type I censored regression.
Chemom Intell Lab Syst 63:117–128. doi:10.1016/S0169-7439(02)00019-9
15. Matthews EJ, Kruhlak NL, Benz RD et al (2008) 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. Toxicol Mech Methods 18:189–206. doi:10.1080/15376510701857379
16. Gottmann E, Kramer S, Pfahringer B, Helma C (2001) Data quality in predictive toxicology:
reproducibility of rodent carcinogenicity experiments. Environ Health Perspect 109:509–514