What Role for Biologically Based Dose–Response Models in Estimating Low-Dose Risk?

Background Biologically based dose–response (BBDR) models can incorporate data on biological processes at the cellular and molecular level to link external exposure to an adverse effect. Objectives Our goal was to examine the utility of BBDR models in estimating low-dose risk. Methods We reviewed the utility of BBDR models in risk assessment. Results BBDR models have been used profitably to evaluate proposed mechanisms of toxicity and identify data gaps. However, these models have not improved the reliability of quantitative predictions of low-dose human risk. In this commentary we identify serious impediments to developing BBDR models for this purpose. BBDR models do not eliminate the need for empirical modeling of the relationship between dose and effect, but only move it from the whole organism to a lower level of biological organization. However, in doing this, BBDR models introduce significant new sources of uncertainty. Quantitative inferences are limited by inter- and intraindividual heterogeneity that cannot be eliminated with available or reasonably anticipated experimental techniques. BBDR modeling does not avoid uncertainties in the mechanisms of toxicity relevant to low-level human exposures. Although implementation of BBDR models for low-dose risk estimation have thus far been limited mainly to cancer modeled using a two-stage clonal expansion framework, these problems are expected to be present in all attempts at BBDR modeling. Conclusions The problems discussed here appear so intractable that we conclude that BBDR models are unlikely to be fruitful in reducing uncertainty in quantitative estimates of human risk from low-level exposures in the foreseeable future. Use of in vitro data from recent advances in molecular toxicology in BBDR models is not likely to remove these problems and will introduce new issues regarding extrapolation of data from in vitro systems.

[1]  L Edler,et al.  Modeling the number and size of hepatic focal lesions following exposure to 2,3,7,8-TCDD. , 1996, Toxicology and applied pharmacology.

[2]  Julia S Kimbell,et al.  Human respiratory tract cancer risks of inhaled formaldehyde: dose-response predictions derived from biologically-motivated computational modeling of a combined rodent and human dataset. , 2004, Toxicological sciences : an official journal of the Society of Toxicology.

[3]  A Kopp-Schneider,et al.  A multistage model of carcinogenesis incorporating DNA damage and repair. , 1991, Risk analysis : an official publication of the Society for Risk Analysis.

[4]  Steven K. Gibb Toxicity testing in the 21st century: a vision and a strategy. , 2008, Reproductive toxicology.

[5]  U. Epa Guidelines for carcinogen risk assessment , 1986 .

[6]  Julia S Kimbell,et al.  Biologically motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. , 2003, Toxicological sciences : an official journal of the Society of Toxicology.

[7]  Annette Kopp-Schneider,et al.  Stochastic Carcinogenesis Models , 2006 .

[8]  S H Moolgavkar,et al.  Cigarette smoking and lung cancer: reanalysis of the British doctors' data. , 1989, Journal of the National Cancer Institute.

[9]  Rory B Conolly,et al.  Commentary on “Toxicity Testing in the 21st Century: Implications for Human Health Risk Assessment” by Krewski et al. , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  C W Chen,et al.  Biologically based dose-response model for liver tumors induced by trichloroethylene. , 2000, Environmental health perspectives.

[11]  E G Luebeck,et al.  Quantitative assessment of the risk of lung cancer associated with occupational exposure to refractory ceramic fibers. , 1999, Risk analysis : an official publication of the Society for Risk Analysis.

[12]  E G Luebeck,et al.  Estimation of Unit Risk for Coke Oven Emissions , 1998, Risk analysis : an official publication of the Society for Risk Analysis.

[13]  E G Luebeck,et al.  Growth kinetics of enzyme-altered liver foci in rats treated with phenobarbital or alpha-hexachlorocyclohexane. , 1995, Toxicology and applied pharmacology.

[14]  Jonathan I Levy,et al.  Science and Decisions: Advancing Risk Assessment , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[15]  Rafael Meza,et al.  Stochastic Modeling of Carcinogenesis , 2010 .

[16]  W Farland,et al.  Incorporating cell proliferation in quantitative cancer risk assessment: approaches, issues, and uncertainties. , 1991, Progress in clinical and biological research.

[17]  R. McClellan A commentary on the NRC report "Science and judgment in risk assessment". , 1994, Regulatory toxicology and pharmacology : RTP.

[18]  W. Y. Tan,et al.  Stochastic modeling of carcinogenesis: Some new insights , 1998 .

[19]  E M Faustman,et al.  A systems-based computational model for dose-response comparisons of two mode of action hypotheses for ethanol-induced neurodevelopmental toxicity. , 2005, Toxicological sciences : an official journal of the Society of Toxicology.

[20]  A Kopp-Schneider,et al.  A model for hepatocarcinogenesis treating phenotypical changes in focal hepatocellular lesions as epigenetic events. , 1998, Mathematical biosciences.

[21]  E. Luebeck,et al.  Effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin on initiation and promotion of GST-P-positive foci in rat liver: A quantitative analysis of experimental data using a stochastic model. , 2000, Toxicology and applied pharmacology.

[22]  D Krewski,et al.  Radon, Cigarette Smoke, and Lung Cancer: A Re‐analysis of the Colorado Plateau Uranium Miners' Data , 1993, Epidemiology.

[23]  Alvin M. Weinberg,et al.  Science and trans-science , 1972, Nature.

[24]  Günter Oberdörster,et al.  SELECTION OF MODELS FOR ASSESSING DOSE-RESPONSE RELATIONSHIPS FOR PARTICLE-INDUCED LUNG CANCER , 1996 .

[25]  Ravi P Subramaniam,et al.  Uncertainties in the CIIT Model for Formaldehyde‐Induced Carcinogenicity in the Rat: A Limited Sensitivity Analysis–I , 2007, Risk analysis : an official publication of the Society for Risk Analysis.

[26]  C W Chen,et al.  A stochastic two-stage carcinogenesis model: a new approach to computing the probability of observing tumor in animal bioassays. , 1991, Mathematical biosciences.

[27]  Division on Earth Risk Assessment in the Federal Government: Managing the Process , 1983 .

[28]  Robert J Kavlock,et al.  Toxicity Testing in the 21st Century: Implications for Human Health Risk Assessment , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[29]  Stephan Morgenthaler,et al.  Heterogeneity in multistage carcinogenesis and mixture modeling , 2008, Theoretical Biology and Medical Modelling.

[30]  Harvey J Clewell,et al.  How can biologically-based modeling of arsenic kinetics and dynamics inform the risk assessment process? - A workshop review. , 2008, Toxicology and applied pharmacology.

[31]  S H Moolgavkar,et al.  A stochastic two-stage model for cancer risk assessment. I. The hazard function and the probability of tumor. , 1988, Risk analysis : an official publication of the Society for Risk Analysis.

[32]  E G Luebeck,et al.  Quantitative analysis of enzyme-altered liver foci in rats initiated with diethylnitrosamine and promoted with 2,3,7,8-tetrachlorodibenzo-p-dioxin or 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin. , 1996, Toxicology and applied pharmacology.

[33]  D Hattis,et al.  Formaldehyde risk assessment. , 1984, Science.

[34]  K S Crump,et al.  Use of mechanistic models to estimate low-dose cancer risks. , 1994, Risk analysis : an official publication of the Society for Risk Analysis.

[35]  M E Andersen,et al.  Hepatic foci in rats after diethylnitrosamine initiation and 2,3,7,8-tetrachlorodibenzo-p-dioxin promotion: evaluation of a quantitative two-cell model and of CYP 1A1/1A2 as a dosimeter. , 1997, Toxicology and applied pharmacology.

[36]  K S Crump,et al.  Limitations of biological models of carcinogenesis for low-dose extrapolation. , 1994, Risk analysis : an official publication of the Society for Risk Analysis.

[37]  R J Kavlock,et al.  Toward a biologically based dose-response model for developmental toxicity of 5-fluorouracil in the rat: a mathematical construct. , 2001, Toxicological sciences : an official journal of the Society of Toxicology.

[38]  Ravi Subramaniam,et al.  Sensitivity analysis of biologically motivated model for formaldehyde-induced respiratory cancer in humans. , 2008, The Annals of occupational hygiene.

[39]  F. Collins,et al.  Transforming Environmental Health Protection , 2008, Science.

[40]  Melvin E. Andersen,et al.  Competitive Inhibition of Thyroidal Uptake of Dietary Iodide by Perchlorate Does Not Describe Perturbations in Rat Serum Total T4 and TSH , 2009, Environmental health perspectives.