2 Background, Tasks, Modeling Methods …
31
provides valuable information as experimental evidences, which could serve as validating benchmarks for in silico models.
Current explanatory or prediction models/schemes for joint toxic effects are
coarse, and predicted toxic thresholds can only be adopted in a conservative manner over a large safety factor [24]. Systems toxicology might shed light on the joint
toxic effects [96]. Except for molecular models, almost all the macroscale models are designed for just one chemical. If reliance on so-called safety factors is to
be diminished and models based on transparent mechanisms are to be emphasized
in the future toxicology, novel models with associated in silico objects have to be
developed, which shall allow reasonable characterization of the dynamic interaction
network of multiple queried chemicals.
2.5 Conclusions and Perspectives
Currently, except for QSAR models, computational toxicology models are rarely
employed in real practice for chemicals risk assessment. Nevertheless, the modeling
framework of computational toxicology has envisioned an attractive paradigm for
future toxicity testing and toxicological studies. With previously described realitymirroring in silico models, general rules that are transferable among similar cases
at the same spatial level can be modeled by well-understood mathematics or logic
rather than obscurely descriptive paragraphs. Although challenges remain for computational toxicology, the endeavor to overcome these challenges will definitely result
in continuous innovation and prosperous development for the field of both chemicals
risk assessment and toxicology.
References
1. UNEP (2013) Global chemicals outlook—towards sound management of chemicals. United
Nations Environment Programme, Nairobi
2. Rappaport SM, Smith MT (2010) Environment and disease risks. Science 330(6003):460–461
3. Schwarzman MR, Wilson MP (2009) New science for chemicals policy. Science
326(5956):1065–1066
4. EU (2006) Regulation (EC) No. 1907/2006 of the European Parliament and of the Council of
18 December 2006, concerning the Registration, Evaluation, Authorization, and Restriction of
Chemicals (REACH). Official Journal of the EU, EU, Brussels
5. Hartung T (2009) Toxicology for the twenty-first century. Nature 460(7252):208–212
6. Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, Dellarco V, Henry T, Holderman
T, Sayre P, Tan S, Carpenter T, Smith E (2009) The toxicity data landscape for environmental
chemicals. Environ Health Perspect 117(5):685–695
7. Collins FS, Gray GM, Bucher JR (2008) Toxicology—transforming environmental health protection. Science 319(5865):906–907
8. NRC (2007) Toxicity testing in the 21st century: a vision and a strategy. National Research
Council, Washington, DC
31
provides valuable information as experimental evidences, which could serve as validating benchmarks for in silico models.
Current explanatory or prediction models/schemes for joint toxic effects are
coarse, and predicted toxic thresholds can only be adopted in a conservative manner over a large safety factor [24]. Systems toxicology might shed light on the joint
toxic effects [96]. Except for molecular models, almost all the macroscale models are designed for just one chemical. If reliance on so-called safety factors is to
be diminished and models based on transparent mechanisms are to be emphasized
in the future toxicology, novel models with associated in silico objects have to be
developed, which shall allow reasonable characterization of the dynamic interaction
network of multiple queried chemicals.
2.5 Conclusions and Perspectives
Currently, except for QSAR models, computational toxicology models are rarely
employed in real practice for chemicals risk assessment. Nevertheless, the modeling
framework of computational toxicology has envisioned an attractive paradigm for
future toxicity testing and toxicological studies. With previously described realitymirroring in silico models, general rules that are transferable among similar cases
at the same spatial level can be modeled by well-understood mathematics or logic
rather than obscurely descriptive paragraphs. Although challenges remain for computational toxicology, the endeavor to overcome these challenges will definitely result
in continuous innovation and prosperous development for the field of both chemicals
risk assessment and toxicology.
References
1. UNEP (2013) Global chemicals outlook—towards sound management of chemicals. United
Nations Environment Programme, Nairobi
2. Rappaport SM, Smith MT (2010) Environment and disease risks. Science 330(6003):460–461
3. Schwarzman MR, Wilson MP (2009) New science for chemicals policy. Science
326(5956):1065–1066
4. EU (2006) Regulation (EC) No. 1907/2006 of the European Parliament and of the Council of
18 December 2006, concerning the Registration, Evaluation, Authorization, and Restriction of
Chemicals (REACH). Official Journal of the EU, EU, Brussels
5. Hartung T (2009) Toxicology for the twenty-first century. Nature 460(7252):208–212
6. Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, Dellarco V, Henry T, Holderman
T, Sayre P, Tan S, Carpenter T, Smith E (2009) The toxicity data landscape for environmental
chemicals. Environ Health Perspect 117(5):685–695
7. Collins FS, Gray GM, Bucher JR (2008) Toxicology—transforming environmental health protection. Science 319(5865):906–907
8. NRC (2007) Toxicity testing in the 21st century: a vision and a strategy. National Research
Council, Washington, DC