Improving cancer dose-response characterization by using physiologically based pharmacokinetic modeling: an analysis of pooled data for acrylonitrile-induced brain tumors to assess cancer potency in the rat.
Historically, U.S. regulators have derived cancer slope factors by using applied dose and tumor response data from a single key bioassay or by averaging the cancer slope factors of several key bioassays. Recent changes in U.S. Environmental Protection Agency (EPA) guidelines for cancer risk assessment have acknowledged the value of better use of mechanistic data and better dose-response characterization. However, agency guidelines may benefit from additional considerations presented in this paper. An exploratory study was conducted by using rat brain tumor data for acrylonitrile (AN) to investigate the use of physiologically based pharmacokinetic (PBPK) modeling along with pooling of dose-response data across routes of exposure as a means for improving carcinogen risk assessment methods. In this study, two contrasting assessments were conducted for AN-induced brain tumors in the rat on the basis of (1) the EPA's approach, the dose-response relationship was characterized by using administered dose/concentration for each of the key studies assessed individually; and (2) an analysis of the pooled data, the dose-response relationship was characterized by using PBPK-derived internal dose measures for a combined database of ten bioassays. The cancer potencies predicted for AN by the contrasting assessments are remarkably different (i.e., risk-specific doses differ by as much as two to four orders of magnitude), with the pooled data assessments yielding lower values. This result suggests that current carcinogen risk assessment practices overestimate AN cancer potency. This methodology should be equally applicable to other data-rich chemicals in identifying (1) a useful dose measure, (2) an appropriate dose-response model, (3) an acceptable point of departure, and (4) an appropriate method of extrapolation from the range of observation to the range of prediction when a chemical's mode of action remains uncertain.
Kirman
C R
CR
McLaren-Hart/ChemRisk, Cleveland, OH 44124, USA.
Hays
S M
SM
Kedderis
G L
GL
Gargas
M L
ML
Strother
D E
DE
D000181:Acrylonitrile / Q000008:administration & dosage / Q000009:adverse effects* / Q000493:pharmacokinetics; D000393:Air Pollutants / Q000009:adverse effects / Q000493:pharmacokinetics; D000818:Animals; D001681:Biological Assay; D001921:Brain / Q000378:metabolism; D001932:Brain Neoplasms / Q000175:diagnosis*; D002273:Carcinogens / Q000008:administration & dosage / Q000009:adverse effects* / Q000493:pharmacokinetics; D019992:Databases as Topic; D004305:Dose-Response Relationship, Drug; D004781:Environmental Exposure; D005027:Ethylene Oxide / Q000009:adverse effects / Q000031:analogs & derivatives / Q000493:pharmacokinetics; D005544:Forecasting; D017408:Guidelines as Topic; D016014:Linear Models; D008954:Models, Biological; D017711:Nonlinear Dynamics; D051381:Rats; D011916:Rats, Inbred F344; D017207:Rats, Sprague-Dawley; D018570:Risk Assessment; D014481:United States; D014484:United States Environmental Protection Agency; D014874:Water Pollutants, Chemical / Q000008:administration & dosage / Q000009:adverse effects / Q000493:pharmacokinetics
D000393:Air Pollutants; D002273:Carcinogens; D014874:Water Pollutants, Chemical; C033154:2-cyanoethylene oxide; D005027:Ethylene Oxide; D000181:Acrylonitrile